Feature Engineering in Python

Question

How do I know when to apply LabelEncoder() or OneHotEncoder()?

I have used LabelEncoder to encode categorical variable for RandomForestRegressor model and it gives a extremely high mean squared error. I have tried hyperparameter tuning with GridSearchCV and it still gives the same value

Answer 1

According to the documentation, you should avoid using LabelEncoder for this:

This transformer should be used to encode target values, i.e. y, and not the input X.

(Source.)

Therefore, I'd suggest using OneHotEncoder instead.

Answer 2

Knowing when to apply LabelEncoder or OneHotEncoder depends on the nature of the categorical variable and the specific requirements of your machine learning model.

Here are some general guidelines:

1. LabelEncoder: Use LabelEncoder when you have an ordinal categorical variable, meaning the categories have a natural ordering or hierarchy. LabelEncoder assigns a unique integer label to each category, preserving the ordinal relationship. It is commonly used for encoding target variables or when working with algorithms that can directly handle ordinal labels. It's used mainly for preserving the ordinality of the target classes, and not to be used for featurr encoding. For feature encoding, you can use OrdinalEncoder instead.

Check this answer for more insight

2. OneHotEncoder: Use OneHotEncoder when you have nominal categorical variables, meaning the categories have no inherent order or hierarchy. OneHotEncoder creates a binary vector for each category, where a value of 1 indicates the presence of that category and 0 indicates the absence. It is suitable for algorithms that cannot directly handle categorical variables or when you want to avoid imposing any ordinal relationship among the categories.

Now, coming to the issue of high mean squared error (MSE) in your RandomForestRegressor model after using LabelEncoder on a categorical variable, it suggests that the encoded labels may not be appropriate for your model. LabelEncoder assigns arbitrary integer labels to categories without considering their inherent relationship. As a result, the model may perceive a false sense of ordinality or magnitude in the encoded labels, leading to suboptimal results.

In this case, you should consider using OneHotEncoder instead of LabelEncoder for your categorical variable. OneHotEncoder will create binary dummy variables for each category, providing a more suitable representation for your RandomForestRegressor model.

Keep in mind that using OneHotEncoder will expand the dimensionality of your feature matrix, so make sure to handle any potential issues such as multicollinearity or excessive memory usage that may arise from the increased number of features.