Another mistake to avoid in machine learning is using redundant features, which affects your model's performance. This can result in overfitting, lack of clarity in feature importance, and an increase in computational time. In this video, learn how to identify redundant features.
- [Instructor] Another mistake to avoid in machine learning … is using redundant features, … which affects your model's performance. … This can result in overfitting, … lack of clarity in feature importance, … and increase in computational time. … For example, let's say that my goal … is to predict whether a student … has instructor one or instructor two. … Say that I have access to a dataset that contains the SID, … which stands for student ID, calculus 1A grade, … calculus 1B grade, trigonometry grade, … algebra grade, geometry grade and instructor, … which is either one or two … for each student from a set of students. … This will serve as the training data. … Then, say I pick calculus 1A grade, calculus 1B grade, … trigonometry grade, algebra grade, … and geometry grade to be the features, … and I built a model accordingly. … The model will use a student's Calc1A, Calc1B, … trig, algebra and geometry grades to predict … whether they have instructor one or instructor two. … Now, note that the courses Calc1A and Calc1B …
Skill Level Intermediate
1. Avoid Mistakes in Coding Practices
2. Avoid Mistakes in Structuring Code
3. Avoid Mistakes in Handling Data
4. Avoid Mistakes in Machine Learning
Using redundant features1m 45s
Get started with Python1m 7s
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