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How to Normalize and Standardize Time Series Data in Python

How to Normalize and Standardize Time Series Data in Python

Some machine learning algorithms will achieve better performance if your time series data has a consistent scale or distribution. Two techniques that you can use to consistently rescale your time series data are normalization and standardization. In this tutorial, you will discover how you can apply normalization and standardization rescaling to your time series data […]

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Example of Combining Hyperplanes Using an Ensemble

Develop an Intuition for How Ensemble Learning Works

Ensembles are a machine learning method that combine the predictions from multiple models in an effort to achieve better predictive performance. There are many different types of ensembles, although all approaches have two key properties: they require that the contributing models are different so that they make different errors and they combine the predictions in […]

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Softmax Activation Function with Python

Softmax Activation Function with Python

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in […]

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Radius Neighbors Classifier Algorithm With Python

Radius Neighbors Classifier Algorithm With Python

Radius Neighbors Classifier is a classification machine learning algorithm. It is an extension to the k-nearest neighbors algorithm that makes predictions using all examples in the radius of a new example rather than the k-closest neighbors. As such, the radius-based approach to selecting neighbors is more appropriate for sparse data, preventing examples that are far […]

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Linear Discriminant Analysis With Python

Linear Discriminant Analysis With Python

Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of observations for each input variable. A new example is then classified by calculating the conditional probability of it belonging to each class and selecting the class with the highest probability. […]

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Line Plot of Accuracy vs. Hill Climb Optimization Iteration for the Diabetes Dataset

How to Hill Climb the Test Set for Machine Learning

Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model. As an approach to machine learning competitions, it is rightfully frowned upon, and most competition platforms impose limitations to prevent it, which is important. Nevertheless, […]

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Hyperparameter Optimization With Random Search and Grid Search

Hyperparameter Optimization With Random Search and Grid Search

Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of […]

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Histogram of Each Variable in the Diabetes Classification Dataset

How to Selectively Scale Numerical Input Variables for Machine Learning

Many machine learning models perform better when input variables are carefully transformed or scaled prior to modeling. It is convenient, and therefore common, to apply the same data transforms, such as standardization and normalization, equally to all input variables. This can achieve good results on many problems. Nevertheless, better results may be achieved by carefully […]

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