Year: 2023

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Selecting the “best” features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I’ll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end,…

This Edureka tutorial explains the 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, Various techniques used for feature selection like filter methods, wrapper methods. Embedded methods along with practical examples in Python. Explore the feature selection statistics and advantages and disadvantages of feature selection.

Data preprocessing and feature exploration are crucial steps in a modeling workflow. In this tutorial, I will demonstrate how to use Python libraries such as scikit-learn, statsmodels, and matplotlib to perform pre-modeling steps. Topics that will be covered include: missing values, variable types, outlier detection, multicollinearity, interaction terms, and visualizing variable distributions. Finally, I will…

Thank you Dear Professor for making these available to us. Not only do you make it interesting, but you have a way of explaining at a deep level, making the concepts so much clearer for us to grasp.

Great coverage on Random Forest from the business user perspective. This talk will explain best practices for successfully using random forests in the business world. It will focus on (1) best practices for preparing training data for random forests so that random forests can do what they do best and (2) best practices for interpreting…