How to Calculate Precision, Recall, F1, and More for Deep Learning Models

Last Updated on August 27, 2020

Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset.

This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at solving the problem.

The Keras deep learning API model is very limited in terms of the metrics that you can use to report the model performance.

I am frequently asked questions, such as:

How can I calculate the precision and recall for my model?

And:

How can I calculate the F1-score or confusion matrix for my model?

In this tutorial, you will discover how to calculate metrics to evaluate your deep learning neural network model with a step-by-step example.

After completing this tutorial, you will know:

  • How to use the scikit-learn metrics API to evaluate a deep learning model.
  • How to make both class and probability predictions with a final model required by the scikit-learn API.
  • How to calculate precision, recall, F1-score, ROC AUC, and more with the scikit-learn API for a model.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Jan/2020: Updated API for Keras 2.3 and TensorFlow 2.0.
How to Calculate Precision, Recall, F1, and More for Deep Learning Models

How to Calculate Precision, Recall, F1, and More for Deep Learning Models
Photo by John, some rights reserved.

Tutorial Overview

This tutorial is divided into three parts; they are:

  1. Binary Classification Problem
  2. Multilayer Perceptron Model
  3. How to Calculate Model Metrics

Binary Classification Problem

We will use a standard binary classification problem as the basis for this tutorial, called the “two circles” problem.

It is called the two circles problem because the problem is comprised of points that when plotted, show two concentric circles, one for each class. As such, this is an example of a binary classification problem. The problem has two inputs that can be interpreted as x and y coordinates on a graph. Each point belongs to either the inner or outer circle.

The make_circles() function in the scikit-learn library allows you to generate samples from the two circles problem. The “n_samples” argument allows you to specify the number of samples to generate, divided evenly between the two classes. The “noise” argument allows you to specify how much random statistical noise is added to the inputs or coordinates of each point, making the classification task more challenging. The “random_state” argument specifies the seed for the pseudorandom number generator, ensuring that the same samples are generated each time the code is run.

The example below generates 1,000 samples, with 0.1 statistical noise and a seed of 1.

Once generated, we can create a plot of the dataset to get an idea of how challenging the classification task is.

The example below generates samples and plots them, coloring each point according to the class, where points belonging to class 0 (outer circle) are colored blue and points that belong to class 1 (inner circle) are colored orange.

Running the example generates the dataset and plots the points on a graph, clearly showing two concentric circles for points belonging to class 0 and class 1.

Scatter Plot of Samples From the Two Circles Problem

Scatter Plot of Samples From the Two Circles Problem

Multilayer Perceptron Model

We will develop a Multilayer Perceptron, or MLP, model to address the binary classification problem.

This model is not optimized for the problem, but it is skillful (better than random).

After the samples for the dataset are generated, we will split them into two equal parts: one for training the model and one for evaluating the trained model.

Next, we can define our MLP model. The model is simple, expecting 2 input variables from the dataset, a single hidden layer with 100 nodes, and a ReLU activation function, then an output layer with a single node and a sigmoid activation function.

The model will predict a value between 0 and 1 that will be interpreted as to whether the input example belongs to class 0 or class 1.

The model will be fit using the binary cross entropy loss function and we will use the efficient Adam version of stochastic gradient descent. The model will also monitor the classification accuracy metric.

We will fit the model for 300 training epochs with the default batch size of 32 samples and evaluate the performance of the model at the end of each training epoch on the test dataset.

At the end of training, we will evaluate the final model once more on the train and test datasets and report the classification accuracy.

Finally, the performance of the model on the train and test sets recorded during training will be graphed using a line plot, one for each of the loss and the classification accuracy.

Tying all of these elements together, the complete code listing of training and evaluating an MLP on the two circles problem is listed below.

Running the example fits the model very quickly on the CPU (no GPU is required).

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

The model is evaluated, reporting the classification accuracy on the train and test sets of about 83% and 85% respectively.

A figure is created showing two line plots: one for the learning curves of the loss on the train and test sets and one for the classification on the train and test sets.

The plots suggest that the model has a good fit on the problem.

Line Plot Showing Learning Curves of Loss and Accuracy of the MLP on the Two Circles Problem During Training

Line Plot Showing Learning Curves of Loss and Accuracy of the MLP on the Two Circles Problem During Training

How to Calculate Model Metrics

Perhaps you need to evaluate your deep learning neural network model using additional metrics that are not supported by the Keras metrics API.

The Keras metrics API is limited and you may want to calculate metrics such as precision, recall, F1, and more.

One approach to calculating new metrics is to implement them yourself in the Keras API and have Keras calculate them for you during model training and during model evaluation.

For help with this approach, see the tutorial:

This can be technically challenging.

A much simpler alternative is to use your final model to make a prediction for the test dataset, then calculate any metric you wish using the scikit-learn metrics API.

Three metrics, in addition to classification accuracy, that are commonly required for a neural network model on a binary classification problem are:

  • Precision
  • Recall
  • F1 Score

In this section, we will calculate these three metrics, as well as classification accuracy using the scikit-learn metrics API, and we will also calculate three additional metrics that are less common but may be useful. They are:

This is not a complete list of metrics for classification models supported by scikit-learn; nevertheless, calculating these metrics will show you how to calculate any metrics you may require using the scikit-learn API.

For a full list of supported metrics, see:

The example in this section will calculate metrics for an MLP model, but the same code for calculating metrics can be used for other models, such as RNNs and CNNs.

We can use the same code from the previous sections for preparing the dataset, as well as defining and fitting the model. To make the example simpler, we will put the code for these steps into simple function.

First, we can define a function called get_data() that will generate the dataset and split it into train and test sets.

Next, we will define a function called get_model() that will define the MLP model and fit it on the training dataset.

We can then call the get_data() function to prepare the dataset and the get_model() function to fit and return the model.

Now that we have a model fit on the training dataset, we can evaluate it using metrics from the scikit-learn metrics API.

First, we must use the model to make predictions. Most of the metric functions require a comparison between the true class values (e.g. testy) and the predicted class values (yhat_classes). We can predict the class values directly with our model using the predict_classes() function on the model.

Some metrics, like the ROC AUC, require a prediction of class probabilities (yhat_probs). These can be retrieved by calling the predict() function on the model.

For more help with making predictions using a Keras model, see the post:

We can make the class and probability predictions with the model.

The predictions are returned in a two-dimensional array, with one row for each example in the test dataset and one column for the prediction.

The scikit-learn metrics API expects a 1D array of actual and predicted values for comparison, therefore, we must reduce the 2D prediction arrays to 1D arrays.

We are now ready to calculate metrics for our deep learning neural network model. We can start by calculating the classification accuracy, precision, recall, and F1 scores.

Notice that calculating a metric is as simple as choosing the metric that interests us and calling the function passing in the true class values (testy) and the predicted class values (yhat_classes).

We can also calculate some additional metrics, such as the Cohen’s kappa, ROC AUC, and confusion matrix.

Notice that the ROC AUC requires the predicted class probabilities (yhat_probs) as an argument instead of the predicted classes (yhat_classes).

Now that we know how to calculate metrics for a deep learning neural network using the scikit-learn API, we can tie all of these elements together into a complete example, listed below.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example prepares the dataset, fits the model, then calculates and reports the metrics for the model evaluated on the test dataset.

If you need help interpreting a given metric, perhaps start with the “Classification Metrics Guide” in the scikit-learn API documentation: Classification Metrics Guide

Also, checkout the Wikipedia page for your metric; for example: Precision and recall, Wikipedia.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Posts

API

Articles

Summary

In this tutorial, you discovered how to calculate metrics to evaluate your deep learning neural network model with a step-by-step example.

Specifically, you learned:

  • How to use the scikit-learn metrics API to evaluate a deep learning model.
  • How to make both class and probability predictions with a final model required by the scikit-learn API.
  • How to calculate precision, recall, F1-score, ROC, AUC, and more with the scikit-learn API for a model.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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114 Responses to How to Calculate Precision, Recall, F1, and More for Deep Learning Models

  1. JG April 3, 2019 at 10:14 pm #

    Very useful scikit-learn library modules (API), to avoid construct and develop your owns functions. Thanks !!.

    I would appreciate if you can add to this snippet (example) the appropriate code to plot (to visualize) the ROC Curves, confusion matrix, (to determine the best threshold probability to decide where to put the “marker” to decide when it is positive or negative or 0/1).

    Also I understand, those metrics only apply for binary classification (F1, precision, recall, AOC curve)? But I know Cohen`s kappa and confusion matrix also apply for multiclass !. Thank you.

  2. scander90 May 2, 2019 at 7:27 pm #

    i used the code blow to get the model result for F1-score

    nn = MLPClassifier(activation=’relu’,alpha=0.01,hidden_layer_sizes=(20,10))
    print (“F1-Score by Neural Network, threshold =”,threshold ,”:” ,predict(nn,train, y_train, test, y_test))

    now i want to get all the other matrices result accuracy and prediction with Plot but i dont know how i can used in the code above

    • Jason Brownlee May 3, 2019 at 6:19 am #

      What problem are you having exactly?

      • scander90 May 4, 2019 at 1:49 pm #

        thank you so much about your support ..

        from sklearn.neural_network import MLPClassifier
        threshold = 200
        train, y_train, test, y_test = prep(data,threshold)
        nn = MLPClassifier(activation=’relu’,alpha=0.01,hidden_layer_sizes=(20,10))
        print (“F1-Score by Neural Network, threshold =”,threshold ,”:” ,predict(nn,train, y_train, test, y_test))

        i used the code above i got it from your website to get the F1-score of the model now am looking to get the accuracy ,Precision and Recall for the same model

  3. Thb DL May 2, 2019 at 7:53 pm #

    Hello, thank you very much for your website, it helps a lot !

    I have a problem related to this post, may be you can halp me 🙂

    I try to understand why I obtain different metrics using “model.evaluate” vs “model.predict” and then compute the metrics…

    I work on sementic segmentation.

    I have an evaluation set of 24 images.

    I have a custom DICE INDEX metrics defined as :


    def dice_coef(y_true, y_pred):

    y_true_f = K.flatten(y_true)

    y_pred_f = K.flatten(y_pred)

    intersection = K.sum (y_true_f * y_pred_f)

    result =(2 * intersection)+1 / (K.sum(y_true_f) + K.sum(y_pred_f))+1

    return result

    When I use model.evaluate, I obtain a dice score of 0.9093835949897766.

    When I use model.predict and then compute the metrics, I obtain a dice score of 0.9092264051238695.

    To give more precisions : I set a batchsize of 24 in model.predict as well as in model.evaluate to be sure the problem is not caused by batch size. I do not know what happen when the batch size is larger (ex: 32) than the number of sample in evaluation set…

    Finaly, to compute the metrics after model.prediction, I run :


    dice_result = 0
    for y_i in range(len(y)):
    dice_result += tf.Session().run(tf.cast(dice_coef(y[y_i], preds[y_i]),
    tf.float32))
    tf.Session().close
    dice_result /= (len(y))

    I thought about the tf.float32 casting to be the cause of the difference ?
    (Maybe “model.evaluate” computes all with tensorflow tensor and return a float at the end whereas I cast tensor in float32 at every loop ? …)

    Do you think about an explanation ?

    Thank you for your help.

    Cheers !

    Thibault

    • Jason Brownlee May 3, 2019 at 6:20 am #

      I suspect the evaluate score is averaging across batches.

      Perhaps take use predict then calculate the score on all predictions.

      • Thb DL May 7, 2019 at 5:42 am #

        Thank you for your reply.

        I just have 24 images in my evaluation set, so if “model.evaluate” compute across batches, with a batch size of 24, it will compute the metric in one time on the whole evaluation set. So it will normally gives the same results than “model.predict” followed by the metric computation on the evaluation set ?

        That’s why I do not understand my differences here.

        Have a good day.

        Thibault

        • Jason Brownlee May 7, 2019 at 6:21 am #

          I recommend calling predict, then calling the sklearn metric of choice with the results.

          • Thb DL May 9, 2019 at 6:46 pm #

            Ok 🙂

            If I finally decide not to use my dice personal score, but rather to trust Sklearn, is it possible to use this biblioteque with Keras during the training?
            Indeed, at the end of the training I get a graph showing the loss and the dice during the epochs.
            I would like these graphs to be consistent with the final results?

            Thanks again for help!

            Have a good day

            Thibault

          • Jason Brownlee May 10, 2019 at 8:15 am #

            I would expect the graphs to be a fair summary of the training performance.

            For presenting an algorithm, I recommend using a final model to make predictions, and plot the results anew.

          • Thb DL May 10, 2019 at 12:06 am #

            Ok, I worked on this today.

            I fixed this problem. Just in case someone alse has a similar problem.

            The fact was that when I resized my ground truth masks before feeding the network with, I did not threshold after the resizing, so I got other values than 0 and 1 at the edges, and my custom dice score gives bad results.

            Now I put the threshold just after the resizing and have same results for all the functions I use !

            Also, be careful with types casting (float32 vs float64 vs int) !

            Anyway, I thank you very much for your disponibility.

            Have a good daye

          • Jason Brownlee May 10, 2019 at 8:18 am #

            Well done!

  4. Jianhong Cheng May 14, 2019 at 11:02 am #

    How to calculate Precision, Recall, F1, and AUC for multi-class classification Problem

    • Jason Brownlee May 14, 2019 at 2:29 pm #

      You can use the same approach, the scores are averaged across the classes.

      • Erica Rac July 17, 2019 at 5:34 am #

        Your lessons are extremely informative, Professor. I am trying to use this approach to calculate the F1 score for a multi-class classification problem but I keep receiving the error message:
        “ValueError: Classification metrics can’t handle a mix of multilabel-indicator and binary targets” I would very much appreciate if you please guide me to what I am doing wrong? Here is the relevant code:

        # generate and prepare the dataset
        def get_data():
        n_test = 280
        Xtrain, Xtest = X[:n_test, :], X[n_test:, :]
        ytrain, ytest = y[:n_test], y[n_test:]
        return X_train, y_train, X_test, y_test

        # define and fit the model
        def get_model(Xtrain, ytrain):
        model = Sequential()
        model.add(Embedding(max_words, embedding_dim, input_length=max_sequence_length))
        model.add(SpatialDropout1D(0.2))
        model.add(LSTM(150, dropout=0.2, recurrent_dropout=0.2))
        model.add(Dense(5, activation=’softmax’))
        model.compile(loss=’categorical_crossentropy’, optimizer= “adam”, metrics=[‘accuracy’])
        model.fit(X_train, y_train, epochs=2, batch_size=15,callbacks=[EarlyStopping(monitor=’loss’)])
        return model

        # generate data
        X_train, y_train, X_test, y_test = get_data()

        # fit model
        model = get_model(X_train, y_train)

        # predict probabilities for test set
        yhat_probs = model.predict(X_test, verbose=0)

        # predict crisp classes for test set
        yhat_classes = model.predict_classes(X_test, verbose=0)

        # reduce to 1d array
        yhat_probs = yhat_probs.flatten()
        yhat_classes = yhat_classes.flatten()

        # accuracy: (tp + tn) / (p + n)
        accuracy = accuracy_score(y_test, yhat_classes)
        print(‘Accuracy: %f’ % accuracy)
        # precision tp / (tp + fp)
        precision = precision_score(y_test, yhat_classes)
        print(‘Precision: %f’ % precision)
        # recall: tp / (tp + fn)
        recall = recall_score(y_test, yhat_classes)
        print(‘Recall: %f’ % recall)
        # f1: 2 tp / (2 tp + fp + fn)
        f1 = f1_score(y_test, yhat_classes)
        print(‘F1 score: %f’ % f1)

        # kappa
        kappa = cohen_kappa_score(testy, yhat_classes)
        print(‘Cohens kappa: %f’ % kappa)
        # ROC AUC
        auc = roc_auc_score(testy, yhat_probs)
        print(‘ROC AUC: %f’ % auc)
        # confusion matrix
        matrix = confusion_matrix(y_test, yhat_classes)
        print(matrix)

        • Jason Brownlee July 17, 2019 at 8:31 am #

          Perhaps check your data matches the expectation of the measures you intend to use?

          • Erica Rac July 17, 2019 at 11:51 am #

            I see my error in preprocessing. Thanks for the quick reply!

          • Jason Brownlee July 17, 2019 at 2:24 pm #

            Happy to hear that.

        • Purnima Khurana May 11, 2020 at 4:29 pm #

          hi @Eric Rac .
          I am getting the same error. How you have corrected it for multiclass classification.

      • Gilbert Gutabaga December 26, 2019 at 1:37 pm #

        Hello i tried the same approach but i end up getting error message ‘Classification metrics can’t handle a mix of multilabel-indicator and multiclass targets ‘

  5. Despina M May 17, 2019 at 4:03 am #

    Hello! Another great post of you! Thank you!

    I want to calculate Precision, Recall, F1 for every class not only the average. Is it possible?

    Thank you in advance

    • Jason Brownlee May 17, 2019 at 5:59 am #

      Yes, I believe the sklearn classification report will provide this information.

      I also suspect you can configure the sklearn functions for each metric to report per-class scores.

      • Despina M May 17, 2019 at 6:49 am #

        Thank you so much for the quick answer! I will try to calculate them.

  6. Despina M May 19, 2019 at 6:07 am #

    I used

    from sklearn.metrics import precision_recall_fscore_support

    precision_recall_fscore_support(y_test, y_pred, average=None)

    print(classification_report(y_test, y_pred, labels=[0, 1]))

    It works fine for me.

    Thanks again!

  7. Vani May 23, 2019 at 10:28 pm #

    How is that accuracy calculated using “history.history[‘val_acc’]” provides different values as compared to accuracy calculated using “accuracy = accuracy_score(testy, yhat_classes)” ?

    • Jason Brownlee May 24, 2019 at 7:51 am #

      It should be the same, e.g. calculate score at the end of each epoch.

      • Vani June 3, 2019 at 1:51 pm #

        thank you

  8. usama May 25, 2019 at 9:53 pm #

    hi jason, i need a code of RNN through which i can find out the classification and confusion matrix of a specific dataset.

  9. vani venk June 3, 2019 at 1:57 pm #

    I calculated accuracy, precision,recall and f1 using following formulas.

    accuracy = metrics.accuracy_score(true_classes, predicted_classes)
    precision=metrics.precision_score(true_classes, predicted_classes)
    recall=metrics.recall_score(true_classes, predicted_classes)
    f1=metrics.f1_score(true_classes, predicted_classes)

    The metrics stays at very low value of around 49% to 52 % even after increasing the number of nodes and performing all kinds of tweaking.

    Eg:
    precision recall f1-score support

    nu 0.49 0.34 0.40 2814
    u 0.50 0.65 0.56 2814

    avg / total 0.49 0.49 0.48 5628

    The confusion matrix shows very high values of FP and FN
    confusion= [[ 953 1861]
    [ 984 1830]]

    What can I do to improve the performance?

    • Vani June 3, 2019 at 2:02 pm #

      For the low values of accuracy, precision, recall and F1, the accuracy and loss plot is also weird.
      The accuracy of validation dataset remains higher than training dataset; similarly, the validation loss remains lower than that of training dataset; whereas the reverse is expected.
      How to overcome this problem?

      • Jason Brownlee June 3, 2019 at 2:35 pm #

        Better results on the test set than the training set may suggest that the test set is not representative of the problem, e.g. is too small.

    • Jason Brownlee June 3, 2019 at 2:35 pm #

      I offer some suggestions here:
      https://machinelearningmastery.com/start-here/#better

  10. onyeka July 27, 2019 at 5:23 pm #

    ValueError: Error when checking input: expected dense_74_input to have shape (2,) but got array with shape (10,)

    i got this error and i dont know what to do next

  11. Khalil September 5, 2019 at 11:05 pm #

    I’m doing a binary text classification, my X_val shape is (85, 1, 62, 300) and my Y_val shape is (85, 2). I get an error when executing this line:

    yhat_classes = saved_model.predict_classes(X_val, verbose=0)

    AttributeError: ‘Model’ object has no attribute ‘predict_classes’

    My snippet code bellow:
    cv_scores, models_history = list(), list()
    start_time = time.time()
    for train, test in myCViterator:
    # Spliting our data
    X_train, X_val, y_train, y_val = df_claim.loc[train].word.tolist(), df_claim.loc[test].word.tolist(), df_label.loc[train].fact.tolist(), df_label.loc[test].fact.tolist()

    X_train = np.array(X_train)
    X_val = np.array(X_val)

    y_train = np.array(y_train)
    y_val = np.array(y_val)

    # Evaluating our model
    model_history, val_acc, saved_model = evaluate_model(X_train, X_val, y_train, y_val)

    # plot loss during training
    pyplot.subplot(211)
    pyplot.title(‘Loss’)
    pyplot.plot(model_history.history[‘loss’], label=’train’)
    pyplot.plot(model_history.history[‘val_loss’], label=’test’)
    pyplot.legend()
    pyplot.show()
    # plot accuracy during training
    pyplot.subplot(212)
    pyplot.title(‘Accuracy’)
    pyplot.plot(model_history.history[‘acc’], label=’train’)
    pyplot.plot(model_history.history[‘val_acc’], label=’test’)
    pyplot.legend()
    pyplot.show()

    print(“\n Metrics for this model:”)
    print(‘> Accuracy: %.3f’ % val_acc)

    cv_scores.append(val_acc)
    models_history.append(model_history)

    print(y_val.shape)
    # Scikit-learn metrics:
    # predict probabilities for test set
    yhat_probs = saved_model.predict(X_val, verbose=0)
    # predict crisp classes for test set
    #yhat_classes = np.argmax(yhat_probs, axis=1)
    yhat_classes = saved_model.predict_classes(X_val, verbose=0)

    # reduce to 1d array
    yhat_probs = yhat_probs[:, 0]
    #yhat_classes = yhat_classes[:, 0]

    # accuracy: (tp + tn) / (p + n)
    accuracy = accuracy_score(y_val, yhat_classes)
    print(‘> Accuracy: %f’ % accuracy)

    # precision tp / (tp + fp)
    precision = precision_score(y_val, yhat_classes)
    print(‘> Precision: %f’ % precision)

    # recall: tp / (tp + fn)
    recall = recall_score(y_val, yhat_classes)
    print(‘> Recall: %f’ % recall)

    # f1: 2 tp / (2 tp + fp + fn)
    f1 = f1_score(y_val, yhat_classes)
    print(‘> F1 score: %f’ % f1)

    # kappa
    kappa = cohen_kappa_score(y_val, yhat_classes)
    print(‘> Cohens kappa: %f’ % kappa)

    # ROC AUC
    auc = roc_auc_score(y_val, yhat_probs)
    print(‘> ROC AUC: %f’ % auc)

    # confusion matrix
    matrix = confusion_matrix(y_val, yhat_classes)
    print(matrix)

    print(“\n\n”)

    print(“— %s seconds —” % (time.time() – start_time))
    print(‘Estimated Accuracy for 5-Folds Cross-Validation: %.3f (%.3f)’ % (np.mean(cv_scores), np.std(cv_scores)))

    • Jason Brownlee September 6, 2019 at 5:02 am #

      If your model is wrapped by scikit-learn, then predict_classes() is not available, it is function on the Keras model. Instead, you can use predict().

      • Khalil September 7, 2019 at 5:58 am #

        I’ve tried to use the predict() method and then get the argmax of the vector (with yhat_classes = np.argmax(yhat_probs, axis=1) ) but then it gives me another error when trying to get the accuracy:

        accuracy = accuracy_score(y_val, yhat_classes)

        ValueError: Classification metrics can’t handle a mix of multilabel-indicator and binary targets

        • Khalil September 7, 2019 at 6:06 am #

          I found the solution, obviously I need to reduce ‘y_val’ to 1d array as well lol.
          Thank you so much for your help and for this great post!

          • Jason Brownlee September 8, 2019 at 5:07 am #

            I’m happy to hear that!

          • vinc March 31, 2020 at 9:17 am #

            Hello, can you explain me how you fixed the problem?
            maybe I have the same problem but I am not able to fix it.
            Really thanks!

  12. NguWah September 14, 2019 at 12:47 am #

    Hello! I trained and got the different accuracy form the model.fit() and model.evaluate() methods. What is the problem? How can I get the right accuracy between this?

    • Jason Brownlee September 14, 2019 at 6:21 am #

      During fit, scores are estimated averaged over batches of samples.

      Use evaluate() to get a true evaluation of the model’s performance.

      • NguWah September 14, 2019 at 7:19 pm #

        Thanks Jason

  13. Ze Rar September 14, 2019 at 7:25 pm #

    Hi Jason
    I got the validation accuracy and test accuracy which are better than the train accuracy without dropout. What can be the problems?

    • Jason Brownlee September 15, 2019 at 6:20 am #

      Perhaps the test or validation dataset are too small and the results are statistically noisy?

      • Ze Rar September 15, 2019 at 2:10 pm #

        I also set 40%(0.4) to the test size

  14. Ze Rar September 14, 2019 at 7:50 pm #

    I also set the test size 0.4

  15. Mesho October 3, 2019 at 9:24 pm #

    Thanks a lot for this useful tutorial.
    I was wondering How to Calculate Precision, Recall, F1 in Multi-label CNN.
    I mean having these Metrics for each label in the output.

    Many thanks for your help.

    • Jason Brownlee October 4, 2019 at 5:41 am #

      I believe the above tutorial shows you how to calculate these metrics.

      Once calculated, you can print the result with your own labels.

  16. PC November 17, 2019 at 3:55 pm #

    Hi Jason,

    Whenever I have doubts related to ML your articles are always there to clarify those. Thank you very much.

    My question is : Can I plot a graph of the Kappa error metric of classifiers?

  17. HSA December 8, 2019 at 2:01 am #

    I wonder how to upload a figure in my response, However, my Line Plot Showing Learning Curves of Loss and Accuracy is very different the training and testing lines do not appear above each other like your plot, they have totally different directions opposite each other.
    what could be the problem given that I tested your code on two different datasets, one is balanced (with 70% f1-score ) and the other is not (with 33% f1-score)?

    • Jason Brownlee December 8, 2019 at 6:15 am #

      You can upload an image to social media, github or an image hosting service like imgur.

      Not sure I follow your question, sorry. Perhaps you can elaborate?

  18. Miao February 6, 2020 at 3:06 pm #

    Thank you for your nice post. I have one question. if we use the one-hot encoder to process labels by using np_utils.to_categorical of Keras in the preprocessing, how to use model.predict()?

    • Jason Brownlee February 7, 2020 at 8:08 am #

      Sorry I don’t understand, they are not related. What is the problem exactly?

      • Miao February 7, 2020 at 12:52 pm #

        Sorry I did not describe my question clearly.
        In the example of the post, if the label was one hot encoded, and the argmax value was taken to predict when using model.predict().
        the code is as below, in this code, the yhat_classes can not be taken argmax, so I think the model.predict_classes() can not used in the one-hot encoder labels.
        And the results here is not better than the results in your post.
        My question is that whether to use on-hot encoder when using model.predict()?

        from sklearn.datasets import make_circles
        from sklearn.metrics import accuracy_score
        from sklearn.metrics import precision_score
        from sklearn.metrics import recall_score
        from sklearn.metrics import f1_score
        from sklearn.metrics import cohen_kappa_score
        from sklearn.metrics import roc_auc_score
        from sklearn.metrics import confusion_matrix
        from keras.models import Sequential
        from keras.layers import Dense
        import keras
        import numpy as np

        # generate and prepare the dataset
        def get_data():
        # generate dataset
        X, y = make_circles(n_samples=1000, noise=0.1, random_state=1)
        # split into train and test
        n_test = 500
        trainX, testX = X[:n_test, :], X[n_test:, :]
        trainy, testy = y[:n_test], y[n_test:]
        return trainX, trainy, testX, testy

        # define and fit the model
        def get_model(trainX, trainy):
        # define model
        model = Sequential()
        model.add(Dense(100, input_dim=2, activation=’relu’))
        model.add(Dense(num_classes, activation=’sigmoid’))
        # compile model
        model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
        # fit model
        model.fit(trainX, trainy, epochs=300, verbose=0)
        return model

        # generate data
        trainX, trainy, testX, testy = get_data()

        # One hot encode labels
        num_classes = 2
        trainy = keras.utils.to_categorical(trainy, num_classes)
        testy = keras.utils.to_categorical(testy, num_classes)

        # fit model
        model = get_model(trainX, trainy)

        # predict probabilities for test set
        yhat_probs = model.predict(testX, verbose=0)
        # predict crisp classes for test set
        yhat_classes = model.predict_classes(testX, verbose=0).reshape(-1,1)

        yhat_probs_inverse = np.argmax(yhat_probs,axis=1).reshape(-1,1)
        testy_inverse = np.argmax(testy, axis=1).reshape(-1,1)

        # reduce to 1d array
        yhat_probs = yhat_probs_inverse[:, 0]
        yhat_classes = yhat_classes[:, 0]

        # accuracy: (tp + tn) / (p + n)
        accuracy = accuracy_score(testy_inverse, yhat_classes)
        print(‘Accuracy: %f’ % accuracy)
        # precision tp / (tp + fp)
        precision = precision_score(testy_inverse, yhat_classes)
        print(‘Precision: %f’ % precision)
        # recall: tp / (tp + fn)
        recall = recall_score(testy_inverse, yhat_classes)
        print(‘Recall: %f’ % recall)
        # f1: 2 tp / (2 tp + fp + fn)
        f1 = f1_score(testy_inverse, yhat_classes)
        print(‘F1 score: %f’ % f1)
        # kappa
        kappa = cohen_kappa_score(testy_inverse, yhat_classes)
        print(‘Cohens kappa: %f’ % kappa)
        # ROC AUC
        auc = roc_auc_score(testy_inverse, yhat_probs_inverse)
        print(‘ROC AUC: %f’ % auc)
        # confusion matrix
        matrix = confusion_matrix(testy_inverse, yhat_classes)
        print(matrix)

        —————————————————————————–
        >>Accuracy: 0.852000
        Precision: 0.858871
        Recall: 0.845238
        F1 score: 0.852000
        Cohens kappa: 0.704019
        ROC AUC: 0.852055
        [[213 35]
        [39 213]]

  19. HSA February 28, 2020 at 2:39 am #

    I used Cohen Kappa to find the inner annotator agreement between two annotator
    rater1 = [0,1,1]
    rater2 = [1,1,1]
    labels=[0,1]
    print(“cohen_kappa_score”,cohen_kappa_score(rater1, rater2, labels=labels))
    why Iam getting 0 result?

    • Jason Brownlee February 28, 2020 at 6:17 am #

      I don’t know off hand, perhaps the prediction has no skill?

      • HSA February 29, 2020 at 12:50 am #

        mmm, I am not working on classification problem, I am working on measuring how the raters agree with each other this is called inner annotator agreement as mentioned here https://en.wikipedia.org/wiki/Cohen%27s_kappa cohen kappa is one of the ways to do that, I am expecting to have a very high value because the annotators opinion is almost similar but I am surprised to have a negative values

  20. Pankaj March 3, 2020 at 9:24 pm #

    I was working with SVHN data base and after using the above code i was getting precison\Recall\F1 of same value.

    which does not looks correct.

    • Jason Brownlee March 4, 2020 at 5:54 am #

      Perhaps try debugging your code to discover the cause of the fault.

  21. Giovanna Fernandes March 15, 2020 at 2:24 am #

    Thank. you, this is very helpful and clear. My only difficulty, which I haven’t found a solution for yet, is how to apply this to a multi-label classification problem?

    My Keras Model (not Sequential) outputs from a Dense layer with a sigmoid activation for 8 possible classes. Samples can be of several classes. If I do a model.predict( ) I get the probabilities for each class:

    pred[0]

    array([0.9876269 , 0.08615541, 0.81185186, 0.6329404 , 0.6115263 ,
    0.11617774, 0.7847705 , 0.9649658 ], dtype=float32)

    My y looks something like this though, a binary classification for each of the 8 classes:

    [1 0 1 1 1 0 1 1]

    predict_classes is only for Sequential, so what can I do in this case in order to get a classification report with precision, recall and f-1 for each class?

    • Giovanna Fernandes March 15, 2020 at 2:31 am #

      Há, nevermind! Sometimes the simplest solutions are right there in front of us and we fail to see them…

      predicted[predicted>=0.5] = 1
      predicted[predicted<0.5] = 0

      Problem solved! 😀

    • Jason Brownlee March 15, 2020 at 6:18 am #

      Yes, for multi-label classification, you get a binary prediction for each label.

      If you want a multi-class classification (mutually exclusive clases), use a softmax activation function instead and an arg max to get the single class.

  22. Rony Sharma March 28, 2020 at 9:24 pm #

    # define model
    model = Sequential()
    model.add(embedding_layer)
    model.add(Conv1D(filters=128, kernel_size=5, activation=’relu’))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(1, activation=’sigmoid’))
    print(model.summary())
    # compile network
    model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
    # fit network
    model.fit(Xtrain, ytrain, epochs=15, verbose=2)
    # evaluate
    loss, acc = model.evaluate(Xtest, ytest, verbose=0)

    # predict probabilities for test set
    yhat_probs = model.predict(Xtest, verbose=0)
    # predict crisp classes for test set
    yhat_classes = model.predict_classes(Xtest, verbose=0)
    # reduce to 1d array
    yhat_probs = yhat_probs[:, 0]
    yhat_classes = yhat_classes[:, 0]

    # accuracy: (tp + tn) / (p + n)
    accuracy = accuracy_score(ytest, yhat_classes)
    print(‘Accuracy: %f’ % accuracy)
    # precision tp / (tp + fp)
    precision = precision_score(ytest, yhat_classes)
    print(‘Precision: %f’ % precision)
    # recall: tp / (tp + fn)
    recall = recall_score(ytest, yhat_classes)
    print(‘Recall: %f’ % recall)
    # f1: 2 tp / (2 tp + fp + fn)
    f1 = f1_score(ytest, yhat_classes)
    print(‘F1 score: %f’ % f1)

    # confusion matrix
    matrix = confusion_matrix(ytest, yhat_classes)
    print(matrix)

    but my output show:

    Accuracy: 0.745556
    Precision: 0.830660
    Recall: 0.776667

    TypeError: ‘numpy.float64’ object is not callable

    how to solve this problem??

  23. Hesham April 10, 2020 at 8:10 pm #

    How to replace make_circles by my data (file.csv) … how to change the code

  24. Law April 18, 2020 at 4:27 am #

    Thank you so much Jason i do enjoy codes a lot, please i will like to know if these metrics Precision, F1 score and Recall can also be applied to Sequence to Sequence prediction with RNN

    • Jason Brownlee April 18, 2020 at 6:12 am #

      Perhaps, but not really. If the output is text, look at metrics like BLEU or ROGUE.

      • Law April 20, 2020 at 3:04 am #

        Ok thanks for your reply, what will your advice for the choice of metrics in RNN sequence to sequence, where the output is number.

        • Jason Brownlee April 20, 2020 at 5:29 am #

          If you are predicting one value per sample, then MAE or RMSE are great metrics to start with.

  25. JONATA PAULINO DA COSTA April 21, 2020 at 6:33 am #

    Hello. I’m doing an SVM algorithm together with a library to learn binary classification. How could I make a f1_score chart with this algorithm?
    Thanks.

  26. JONATA PAULINO DA COSTA April 21, 2020 at 1:04 pm #

    In reality, I know which metric to use, I already know that it is a f1_score, however, I was unable to do it with SVM.

    • Jason Brownlee April 21, 2020 at 1:25 pm #

      Why not?

      • JONATA PAULINO DA COSTA April 21, 2020 at 11:41 pm #

        f1_score em rede neural eu pego cada época e mostro no gráfico, já no SVM não sei como fazer.

        • Jason Brownlee April 22, 2020 at 5:57 am #

          Sorry, I don’t have examples of working with graph data.

  27. JONATA PAULINO DA COSTA April 21, 2020 at 11:42 pm #

    f1_score in neural network I take each season and show it in the graph, already in SVM I don’t know how to do it.

    obs:sorry for the message replication, it was an error.

    • Jason Brownlee April 22, 2020 at 5:58 am #

      If someone has created a report or plot you like, perhaps ask them how they made it?

      • JONATA PAULINO DA COSTA April 22, 2020 at 6:01 am #

        Muito obrigado.

  28. nandini May 11, 2020 at 8:19 pm #

    hi sir,

    How can we achieve 100% recall in deep learning ,
    please suggest any tips to improve the recall part in deep learning.

    more over i am trying on text classification , all datasets having imbalanced ,
    we have applied smote method to overcome imbalanced one ,not getting error smote method .

    is that good way to apply smote method for imbalanced text classification is their any other methods are available to improve recall of imbalanced text classification .

  29. nkm May 22, 2020 at 9:15 pm #

    Hi Mr. Jason,

    Function predict_classes is not available for Keras functional API. Any suggestions please. How to calculate matrices for functional API case?

  30. Karl Demree June 10, 2020 at 3:04 am #

    Some metrics, like the ROC AUC, require a prediction of class probabilities (yhat_probs). These can be retrieved by calling the predict() function on the model.

    I really dont get why yhat_classes isn’t used for ROC AUC as well. It would be great if you could explain this. Also when you say “prediction of class probabilities” shouldn’t we use “predict_proba” rather than just “predict”?.

    Many thanks

    • Jason Brownlee June 10, 2020 at 6:20 am #

      In keras the predict() function returns probabilities on classification tasks:

      • Karl Demree June 11, 2020 at 1:25 am #

        Thanks much! Great tutorial

  31. nkm June 16, 2020 at 4:02 pm #

    Hi Mr. Jason,

    thanks for your great support. I am working on four class classification of images with equal number of images in each class (for testing, total 480 images, 120 in each class). I am calculating metrics viz. accuracy, Precision, Recall and F1-score from test dataset. I used three options to calculate these metrics, first scikit learn API as explained by you, second option is printing classification summary and third using confusion matrix. In all three ways, I am getting same value (0.92) for all fours metrics. Is it possible to get same value for all four metrics or I am doing something wrong. From your experience, kindly clarify and suggest way ahead.

    Thanks ans Regards

    • Jason Brownlee June 17, 2020 at 6:17 am #

      Perhaps. Check that you don’t have a bug in your test harness.

      Also, I recommend selecting one metric and optimize that.

      • nkm June 18, 2020 at 3:06 am #

        Thanks for your quick Reply. I am attaching my test code in hope that your experience will definitely show some solution:

        #Four class classification problem:

        class_labels = list(test_it.class_indices.keys())
        y_true = test_it.classes
        test_it.reset() #
        Y_pred = model.predict(test_it, STEP_SIZE_TEST,verbose=1)
        y_pred1 = np.argmax(Y_pred, axis=1, out=None)
        target_names = [‘Apple’, ‘Orange, ‘Mango’,’Guava’]

        cm = confusion_matrix(y_true, y_pred1)
        print(‘Confusion Matrix’)
        print(cm)

        print(‘Classification Report’)
        print(classification_report(test_it.classes, y_pred1, target_names=class_labels))
        ax= plt.subplot()
        sns.heatmap(cm, annot=True, ax = ax, cmap=’Blues’, fmt=’d’ );
        ax.set_xlabel(‘Predicted labels’);ax.set_ylabel(‘True labels’);
        ax.set_title(‘Confusion Matrix’);
        ax.xaxis.set_ticklabels([‘Apple’, ‘Orange, ‘Mango’,’Guava’]);
        ax.yaxis.set_ticklabels([‘Apple’, ‘Orange, ‘Mango’,’Guava’]);

        accuracy = accuracy_score(y_true, y_pred1)
        print(‘Accuracy: %f’ % accuracy)
        precision = precision_score(y_true, y_pred1, average=’micro’)
        print(‘Precision: %f’ % precision)
        recall = recall_score(y_true, y_pred1, average=’micro’)
        print(‘Recall: %f’ % recall)
        f1 = f1_score(y_true, y_pred1,average=’micro’)
        print(‘F1 score: %f’ % f1)

        With thanks and Regards

  32. nkm June 19, 2020 at 1:18 pm #

    I multiclass classification, can three evaluation metrices (accuracy, precision, recall) converge to the same value?

    In this reference, there is a classification report shown, which has same values for all the three metrices:-

    https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py

  33. salim August 31, 2020 at 6:05 am #

    you are the best

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