A train test split is critical in testing your models. In this video, learn how to perform the train test split procedure using the Python library scikit-learn so you can simulate how well your model performs on new data.
- [Instructor] The goal of machine learning … is it build a model that performs well on new data. … If you have new data, … you can see how well your model performs on it. … The problem is that you may not have new data, … but you can simulate this experience … with Scikit Learn's train test split. … In this video, I'll show you how train test split works … in Scikit Learn. … The first thing that you need to know … is what is train test split? … Here's how that procedure works. … The first step is to split your data into two pieces, … a training set and a testing set. … Typically, about 75% of the data goes to your training set … and about 25% of your data goes to the test set. … The second step of the process … is to train the model on the training set. … The final step is to test the model on the testing set … and evaluate the performance. … To do this in Scikit Learn, … you first have to import libraries. … The next step is to load a dataset. … The dataset using this notebook … is the Boston House Price dataset. …
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)