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 created this model and named it 'model', and now it has a fit method on it. That method fits the data and trains the model on it.
We get back a trained model. It will 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 and the label that corresponds to those features.
This allows the model to classify data such as identifying a cat or a dog. Or, for example, the model might learn that 5 minus 3 equals 2 and 7 minus 4 equals 3, hoping it will detect patterns and relationships. The model needs both the question (input) and the answer (label) to understand the concept.
We provide it with the data and allow it to train on it. It's that simple.
We simply use model.fit. It's straightforward, not necessarily easy. Give it the X train data and the Y train data.
It will evaluate as a linear regression model, as shown here. Now that the model is trained, and it was done quickly, we can begin testing the model to see how well it works.