Avoid these common mistakes when working with Scikit-Learn's Estimator API in Python

When using scikit-learn's estimator API, there are several common mistakes that people make. Here are some examples of these mistakes along with corresponding code snippets:

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

Splitting the data is crucial for evaluating the model’s performance on unseen data which is its ability to generalize well to new data. Here is how you can counter this issue by splitting the data using train_test_split:

2. Not handling missing values:

A common mistake is failing to address missing values which can result in errors or biased models as most machine learning algorithms cannot handle missing values. In the example code below, we have handled the missing values in a dataset using SimpleImputer:

3. Not scaling the features when necessary:

Some machine learning algorithms are sensitive to the scale of the features, a common mistake made by individuals is to skip the scaling step which can result in poor model performance. In the example below, the data is scaled using StandardScaler before training, and then it is tested on testing data.

4. Not properly handling categorical features:

Many machine learning algorithms require numerical inputs and real-world data often have categorical features too. Failing to encode categorical features appropriately can lead to incorrect model behavior. In the example below, we have used LabelEncoder to encode the categorical variables of the data into numerical ones for better performance.