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Day 33 of #100dayswithmachinelearning
Topic - Handling Mixed Variable in Feature Engineering 👨💻
A Thread 🧵
Topic - Handling Mixed Variable in Feature Engineering 👨💻
A Thread 🧵
Handling missing Variable is very important as many machine learning algorithms do not support data with missing values. If you have missing values in the dataset, it can cause errors and poor performance with some machine learning algorithms.
Variable deletion involves dropping variables (columns) with missing values on a case-by-case basis. This method makes sense when there are a lot of missing values in a variable and if the variable is of relatively less importance.
Feature importance gives you a score for each feature of your data. The higher the score, the more important or relevant that feature is to your target feature
It is an inbuilt class that comes with tree-based classifiers such as
Random Forest Classifiers
Extra Tree Classifiers
It is an inbuilt class that comes with tree-based classifiers such as
Random Forest Classifiers
Extra Tree Classifiers
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