Yes, the cross_validate()
function in scikit-learn automatically fits and trains a model when performing the cross-validation on a given model. Cross-validation is a technique used in machine learning and statistics to assess a predictive model’s performance and generalization ability. Its primary purpose is to provide a more robust estimate of a model’s performance than a single train-test split. Essentially, the cross_validate()
function is used to split the training data into multiple folds or subsets, and it uses one of these folds for the validation set, and the rest of the folds are used to train the model.
This is how the cross_validate()
function is typically used:
We provide the function with the machine learning model we want to evaluate, our dataset, and other optional parameters.
It then internally splits the dataset into several folds. The cv
parameter controls the number of subsets.
For all the different subsets, it trains the model and then evaluates the model by testing it on the test subset.
This process is repeated for all the folds, and different evaluation metrics, such as accuracy, precision, recall, etc., are calculated.
Finally, a results dictionary is returned, including each fold’s scores (evaluation metrics).
Here is a basic example of how to use the cross_validate()
function.
from sklearn.model_selection import cross_validatefrom sklearn.linear_model import LogisticRegressionfrom sklearn.datasets import load_iris# Load the Iris datasetdata = load_iris()X = data.datay = data.target# Create a logistic regression modelmodel = LogisticRegression(max_iter=1000)# Perform 5-fold cross-validationresults = cross_validate(model, X, y, cv=5, scoring='accuracy')# Access the results, including scores for each foldprint("Cross-validation results:")print("Test accuracy scores:", results['test_score'])
Line 6: In this part of the code, we simply import the iris dataset using the load_iris()
function.
Lines 7 and 8: We separate the dependent and independent variables from the dataset.
Line 11: We create our very basic LogisticRegression()
model.
Line 14: Finally, we use the cross_validate()
function to train and fit the model for 5 different folds and print out the accuracy score for each.
In conclusion, the cross_validate()
function in scikit-learn streamlines the cross-validation process by automatically generating data splits and fitting our specified machine learning model to each fold. It also trains the model automatically for every fold and helps us evaluate the performance of our model better by allowing us to discern potential issues like overfitting and obtain a more robust understanding of its generalization capabilities.