Machine Learning

3 Ways to Encode Categorical Variables for Deep Learning

Last Updated on August 27, 2020 Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods. In […]

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Machine Learning

How to Perform Feature Selection with Categorical Data

Last Updated on August 18, 2020 Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with real-valued data, such as using the Pearson’s correlation coefficient, but can be challenging when working with categorical data. The two most commonly used feature selection methods for categorical input data when the target variable is also categorical (e.g. classification predictive modeling) are the chi-squared […]

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Machine Learning

How to Choose a Feature Selection Method For Machine Learning

Last Updated on August 20, 2020 Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model. Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics and selecting those input variables that have the strongest relationship […]

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Machine Learning

How to Use an Empirical Distribution Function in Python

Last Updated on August 28, 2020 An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not fit a standard probability distribution. As such, it is sometimes called the empirical cumulative distribution function, or ECDF for short. In this tutorial, you will discover the empirical probability distribution function. After completing this tutorial, you will know: Some data samples cannot be summarized using a standard distribution. An empirical distribution function provides a […]

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Machine Learning

A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset. The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem. Naively, you might believe that model performance is sufficient, but should you consider other concerns, such as how long the model takes to train or how easy it is to explain […]

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Machine Learning

A Gentle Introduction to the Bayes Optimal Classifier

Last Updated on August 19, 2020 The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is described using the Bayes Theorem that provides a principled way for calculating a conditional probability. It is also closely related to the Maximum a Posteriori: a probabilistic framework referred to as MAP that finds the most probable hypothesis for a training dataset. In practice, the Bayes Optimal Classifier is computationally expensive, if not intractable […]

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Machine Learning

How to Use Out-of-Fold Predictions in Machine Learning

Last Updated on August 28, 2020 Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions. Out-of-fold predictions play an important role in machine learning in both estimating the performance of a model when making predictions on new data in the future, so-called the […]

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Machine Learning

Develop an Intuition for Bayes Theorem With Worked Examples

Last Updated on August 19, 2020 Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, providing a method that is easy to use for scenarios where our intuition often fails. The best way to develop an intuition for Bayes Theorem is to think about the meaning of the terms in the equation and to apply the calculation many times in a range of different real-world scenarios. This will provide the context for […]

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Machine Learning

How to Develop Super Learner Ensembles in Python

Last Updated on August 17, 2020 Selecting a machine learning algorithm for a predictive modeling problem involves evaluating many different models and model configurations using k-fold cross-validation. The super learner is an ensemble machine learning algorithm that combines all of the models and model configurations that you might investigate for a predictive modeling problem and uses them to make a prediction as-good-as or better than any single model that you may have investigated. The super learner algorithm is an application […]

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Machine Learning

Tune Hyperparameters for Classification Machine Learning Algorithms

Last Updated on August 28, 2020 Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm. Unlike parameters, hyperparameters are specified by the practitioner when configuring the model. Typically, it is challenging to know what values to use for the hyperparameters of a given algorithm on a given dataset, therefore it […]

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