Welcome! In today's lesson, we are diving into the concept of Mutual Information for Feature Selection within the context of dimensionality reduction. By the end of this lesson, you'll understand how to use Mutual Information to measure the significance of features in a data set, thus leading to more efficient model computation by selecting the most relevant features.
We'll start with a brief introduction to Mutual Information, introduce you to the Wine dataset, demonstrate feature selection using Mutual Information, and finally visualize feature importance using a bar plot. So, let's dive in!
Mutual Information (MI) is a metric that quantifies the "mutual dependence" between two variables. In simpler terms, it measures how much knowing the value of one variable reduces the uncertainty about the value of the other variable. Hence, Mutual Information measures the 'information' that X and Y share.
In the context of machine learning and data science, MI can be used to provide a measure of how much 'information' about the target variable (outcome) is contained within the features. By identifying features that share more 'information' with the target, we can select the most relevant features for our model, leading to improved computational efficiency in training the model. This approach works best when we are dealing with categorical features or mixed data types.
Let's now understand how this is implemented using a real-world dataset.
Before moving on to the implementation, let's understand the algorithm of feature selection using Mutual Information:
- Compute Mutual Information: Calculate the Mutual Information between each feature and the target variable. This step helps identify which features are most informative about the target variable. The higher the Mutual Information value, the more 'information' the feature carries about the target.
- Select Features: Based on the Mutual Information values, select the features that are most informative about the target variable. These features will be used for model training and prediction.
