A Gentle Introduction to Degrees of Freedom in Machine Learning

Last Updated on August 19, 2020 Degrees of freedom is an important concept from statistics and engineering. It is often employed to summarize the number of values used in the calculation of a statistic, such as a sample statistic or in a statistical hypothesis test. In machine learning, the degrees of freedom may refer to the number of parameters in the model, such as the number of coefficients in a linear regression model or the number of weights in a […]

September 29, 2020 Machine Learning

How to Develop a Bagging Ensemble with Python

Last Updated on September 7, 2020 Bagging is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is also easy to implement given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters. Bagging performs well in general and provides the basis for a whole field of ensemble of decision tree algorithms such as the popular random forest and extra trees ensemble algorithms, as well as the lesser-known Pasting, Random Subspaces, and […]

September 29, 2020 Machine Learning

Difference Between Algorithm and Model in Machine Learning

Last Updated on August 19, 2020 Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often “machine learning algorithm” is used interchangeably with “machine learning model.” Are they the same thing or something different? As a developer, your intuition with “algorithms” like sort algorithms and search algorithms will help to clear up this confusion. In this post, you will discover the difference between machine learning “algorithms” and “models.” After reading this […]

September 29, 2020 Machine Learning

How to Develop an AdaBoost Ensemble in Python

Last Updated on August 13, 2020 Boosting is a class of ensemble machine learning algorithms that involve combining the predictions from many weak learners. A weak learner is a model that is very simple, although has some skill on the dataset. Boosting was a theoretical concept long before a practical algorithm could be developed, and the AdaBoost (adaptive boosting) algorithm was the first successful approach for the idea. The AdaBoost algorithm involves using very short (one-level) decision trees as weak […]

September 29, 2020 Machine Learning

How to Develop a Gradient Boosting Machine Ensemble in Python

Last Updated on September 7, 2020 The Gradient Boosting Machine is a powerful ensemble machine learning algorithm that uses decision trees. Boosting is a general ensemble technique that involves sequentially adding models to the ensemble where subsequent models correct the performance of prior models. AdaBoost was the first algorithm to deliver on the promise of boosting. Gradient boosting is a generalization of AdaBoosting, improving the performance of the approach and introducing ideas from bootstrap aggregation to further improve the models, […]

September 29, 2020 Machine Learning

Introduction to Dimensionality Reduction for Machine Learning

Last Updated on June 30, 2020 The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality. High-dimensionality statistics and dimensionality reduction techniques are often used for data visualization. Nevertheless these techniques can be used in applied machine […]

September 29, 2020 Machine Learning

Principal Component Analysis for Dimensionality Reduction in Python

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for short. This is a technique that comes from the field of linear algebra and can be used as a […]

September 29, 2020 Machine Learning

Singular Value Decomposition for Dimensionality Reduction in Python

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the more popular technique for dimensionality reduction in machine learning is Singular Value Decomposition, or SVD for short. This is a technique that comes from the field of linear algebra and can be used as a […]

September 29, 2020 Machine Learning

Linear Discriminant Analysis for Dimensionality Reduction in Python

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique, providing a projection of a training dataset that best separates the examples […]

September 29, 2020 Machine Learning

Statistical Imputation for Missing Values in Machine Learning

Last Updated on August 18, 2020 Datasets may have missing values, and this can cause problems for many machine learning algorithms. As such, it is good practice to identify and replace missing values for each column in your input data prior to modeling your prediction task. This is called missing data imputation, or imputing for short. A popular approach for data imputation is to calculate a statistical value for each column (such as a mean) and replace all missing values […]

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