Learn how to effectively train a linear regression model by supplying it with labeled data. Understand the simple yet crucial steps involved in preparing machine learning models for testing.
Key Insights
- Train a linear regression model using the
fitmethod, providing it with both features (X train) and labels (Y train) so it can accurately learn data patterns. - Supplying the model with labeled data enables it to identify patterns and relationships—for example, distinguishing between categories like "cat" vs. "dog" or solving arithmetic problems such as "five minus three."
- After training the model with labeled data, the next step involves evaluating its performance by testing it against new data samples.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
Let's train the model. We made this model, just named it model, and now it has a fit method on it. And that fits the data, sort of trains it on that data.
And we get back a trained model. It's gonna take our training data as its inputs, and that's the X train and the Y train. Remember, it needs to know the answer as well as the inputs, the features, as well as the label that goes with those features.
That way it can take a look at it and say, okay, that's a cat, that's a dog. Or, class five minus three is two, seven minus four is three, and hope that they can learn the patterns, learn the lessons. They need to know both the question and the answer in order to understand the concept.
So that's what we're doing here. We're going to give it that value and have it train on it. So that's very easy.
We just say model.fit. It's very simple, I should say. Not easy, not the same thing. Give it the X train data and the Y train data.
And it will evaluate to a linear regression model, this right here. And now that it's trained and it was that quick, we can start testing the model, see how it works.