Avoiding common oversights when creating models in Python using Scikit-Learn

When building models in scikit-learn, there are several common mistakes that people can make. Here are a few examples of these mistakes along with corresponding code snippets using various datasets:

1. Not splitting the data into training and testing sets:

Splitting the data is crucial to evaluate the model’s performance on unseen data. Failing to split the data can lead to overfitting and can leave you with no unseen data on which you can test the model on. To tackle this mistake, just split the data into training and testing sets using the train_test_split method of scikit-learn.

2. Ignoring feature scaling:

Many machine learning algorithms benefit from feature scaling and it significantly increases their performance. Failing to scale the features can result in suboptimal performance or convergence issues. Here is an example where we scale the features before training the model:

3. Not handling categorical variables properly:

When dealing with categorical variables, it is essential to encode them appropriately to numeric representations as models work well with numerical data. Here is an example code that creates a random dataset and then encodes its categorical features before training the mode:

4. Not performing cross-validation:

Cross-validation is a technique that helps assess the model’s generalization performance by evaluating it on multiple subsets of the data. Neglecting cross-validation may lead to an over-optimistic estimate of the model’s performance. Here is an example code that performs cross-validation: