Grid search over any machine learning pipeline step using an EstimatorSwitch — A very common step in building a machine learning model is to grid search over a classifier’s parameters on the train set, using cross-validation, to find the most optimal parameters. What is less known, is that you can also grid search over virtually any pipeline step, such as feature engineering…

Unevenly spread Time Series data is no longer a problem for cross-validation — Cross-validation is an often-used method for data scientists who are training a Machine Learning model. To go into the reasons to use cross-validation is beyond the scope of this article, but there are many good articles to find about it, e.g. this one. Below is an illustration of a simple…

Easily get deeper insights into your categorical data by using these two methods — When comparing the distributions of categorical data with a numerical target variable the three most used plots are generally the boxplot, violinplot and stripplot. Let’s take a look at these graphs plotted using Python’s seaborn library and analyze how we can extract more information from these plots. We’ll use a…

Explained through four examples where the “best model” isn’t actually the best model — Scikit-learn’s GridSearchCV is an often-used tool to optimize the hyperparameters of a machine-learning model. Unfortunately, not everyone is analyzing its output thoroughly, and simply uses the GridSearchCV’sbest estimator. This means in a lot of situations you might not be using the actual best estimator. …