Machine Learning in Python - Feature Selection - Step Up Analytics

The data features that we use to train our machine learning models have a huge influence on the performance we can achieve. Irrelevant or partially relevant features can negatively impact model performance. Feature selection is a process where we automatically select those features in our data that contribute most to the prediction variable or output in which we are interested. Having irrelevant features in our data can decrease the accuracy of many models, especially linear algorithms like linear and logistic regression. We can learn more about feature selection with scikit-learn in the article Feature selection.