Machine Learning Mastery
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How to Choose Data Preparation Methods for Machine Learning
Data preparation is an important part of a predictive modeling project. Correct application of data preparation will transform raw data into a...
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8 Top Books on Data Cleaning and Feature Engineering
Data preparation is the transformation of raw data into a form that is more appropriate for modeling. It is a challenging topic to discuss as...
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Feature Engineering and Selection (Book Review)
Data preparation is the process of transforming raw data into learning algorithms. In some cases, data preparation is a required step in order...
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kNN Imputation for Missing Values in Machine Learning
Datasets may have missing values, and this can cause problems for many machine learning algorithms. As such, it is good practice to identify...
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How to Avoid Data Leakage When Performing Data Preparation
Data preparation is the process of transforming raw data into a form that is appropriate for modeling. A naive approach to preparing data...
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Tour of Data Preparation Techniques for Machine Learning
Predictive modeling machine learning projects, such as classification and regression, always involve some form of data preparation. The...
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What Is Data Preparation in a Machine Learning Project
Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and...
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Why Data Preparation Is So Important in Machine Learning
On a predictive modeling project, machine learning algorithms learn a mapping from input variables to a target variable. The most common form...
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Ordinal and One-Hot Encodings for Categorical Data
Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical data, you must...
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How to Use StandardScaler and MinMaxScaler Transforms in Python
Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that...
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How to Perform Feature Selection With Numerical Input Data
Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature...
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Iterative Imputation for Missing Values in Machine Learning
Datasets may have missing values, and this can cause problems for many machine learning algorithms. As such, it is good practice to identify...
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Test-Time Augmentation For Structured Data With Scikit-Learn
Test-time augmentation, or TTA for short, is a technique for improving the skill of predictive models. It is typically used to improve the...
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How to Use Polynomial Feature Transforms for Machine Learning
Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified...
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How to Scale Data With Outliers for Machine Learning
Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that...
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Recursive Feature Elimination (RFE) for Feature Selection in Python
Recursive Feature Elimination , or RFE for short, is a popular feature selection algorithm. RFE is popular because it is easy to configure and...