Prepare your dataset effectively by splitting features and targets for accurate machine learning analysis. Learn to implement training and testing data partitions to ensure robust model performance.
Key Insights
- The article outlines the process of defining feature inputs (X) by selecting columns such as sepal length and width, petal length and width, from the iris dataset.
- It demonstrates assigning the "target" column of the dataset as output labels (Y), resulting in 150 labeled rows categorized numerically (0, 1, and 2).
- The content explains using scikit-learn's train-test split method to randomly partition the dataset into training and testing subsets, specifying a 20% test size to yield 30 randomly selected samples for validation.
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 take a look at preparing our data into X and Y training and testing sets. This looks ready for use. We have our four features: sepal length, sepal width, petal length, and petal width, and we have our target and species to help us better interpret the data.
All right, so X represents the features for training, and that means that X should be the iris dataframe with the columns: sepal length (cm), sepal width (cm), petal length (cm), and petal width (cm). Let's see if we got that right. It's very easy to get a typo there.
It looks like we nailed it. Okay, those are our inputs. Let's get our target, our Y, and that's very simple.
Y is the iris dataframe target, and there it is. 150 rows, zeros, ones, and twos (representing species). Now let's use our train test split as we did previously.
We want X_train, X_test, Y_train, and Y_test to be the train_test_split of our X data, our Y data, and a test size of 0.2 (20%). And we want to take a look at, you know, maybe the test data. Here are the targets for our test data, and you can see that they're now randomly assigned—30 samples, as this is 20% of 150.
And the corresponding test data—30 samples. These are the inputs that go with those answers. All right, next up we'll create our model, train it, and get it working.