Applied machine learning is typically focused on finding a single model that performs well or best on a given dataset. Effective use of the model will require appropriate preparation of the input data and hyperparameter tuning of the model. Collectively, the linear sequence of steps required to prepare the data, tune the model, and transform […]
Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagates […]
Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary […]
Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate […]
The Perceptron is a linear machine learning algorithm for binary classification tasks. It may be considered one of the first and one of the simplest types of artificial neural networks. It is definitely not “deep” learning but is an important building block. Like logistic regression, it can quickly learn a linear separation in feature space […]
PyCaret is a Python open source machine learning library designed to make performing standard tasks in a machine learning project easy. It is a Python version of the Caret machine learning package in R, popular because it allows models to be evaluated, compared, and tuned on a given dataset with just a few lines of […]
Overfitting is a common explanation for the poor performance of a predictive model. An analysis of learning dynamics can help to identify whether a model has overfit the training dataset and may suggest an alternate configuration to use that could result in better predictive performance. Performing an analysis of learning dynamics is straightforward for algorithms […]
Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression involves adding penalties to the loss function during training that encourage simpler models that have smaller coefficient […]
Nearest Centroids is a linear classification machine learning algorithm. It involves predicting a class label for new examples based on which class-based centroid the example is closest to from the training dataset. The Nearest Shrunken Centroids algorithm is an extension that involves shifting class-based centroids toward the centroid of the entire training dataset and removing […]
Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression invokes adding penalties to the loss function during training that encourages simpler models that have smaller coefficient […]
