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  • How to Develop Super Learner Ensembles in Python

    Python Machine Learning • 2019/12/11 02:00

    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 […]

    The post How to Develop Super Learner Ensembles in Python appeared first on Machine Learning Mastery.

  • Develop an Intuition for Bayes Theorem With Worked Examples

    Probability • 2019/12/09 02:00

    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 […]

    The post Develop an Intuition for Bayes Theorem With Worked Examples appeared first on Machine Learning Mastery.

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

    Python Machine Learning • 2019/12/06 02:00

    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 […]

    The post How to Use Out-of-Fold Predictions in Machine Learning appeared first on Machine Learning Mastery.

  • A Gentle Introduction to the Bayes Optimal Classifier

    Probability • 2019/12/04 02:00

    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 […]

    The post A Gentle Introduction to the Bayes Optimal Classifier appeared first on Machine Learning Mastery.

  • A Gentle Introduction to Model Selection for Machine Learning

    Machine Learning Process • 2019/12/02 02:00

    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 […]

    The post A Gentle Introduction to Model Selection for Machine Learning appeared first on Machine Learning Mastery.

  • How to Use an Empirical Distribution Function in Python

    Probability • 2019/11/29 02:00

    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, […]

    The post How to Use an Empirical Distribution Function in Python appeared first on Machine Learning Mastery.

  • How to Choose a Feature Selection Method For Machine Learning

    Python Machine Learning • 2019/11/27 02:00

    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. Feature-based feature selection methods involve evaluating the relationship between […]

    The post How to Choose a Feature Selection Method For Machine Learning appeared first on Machine Learning Mastery.

  • How to Perform Feature Selection with Categorical Data

    Python Machine Learning • 2019/11/25 02:00

    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 […]

    The post How to Perform Feature Selection with Categorical Data appeared first on Machine Learning Mastery.

  • 3 Ways to Encode Categorical Variables for Deep Learning

    Deep Learning • 2019/11/22 02:00

    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 […]

    The post 3 Ways to Encode Categorical Variables for Deep Learning appeared first on Machine Learning Mastery.

  • How to Save and Reuse Data Preparation Objects in Scikit-Learn

    Python Machine Learning • 2019/11/20 02:00

    It is critical that any data preparation performed on a training dataset is also performed on a new dataset in the future. This may include a test dataset when evaluating a model or new data from the domain when using a model to make predictions. Typically, the model fit on the training dataset is saved […]

    The post How to Save and Reuse Data Preparation Objects in Scikit-Learn appeared first on Machine Learning Mastery.

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