Model Evaluation
Now that we have trained our Logistic Regression algorithm, the next step is to assess the performance. We will compare the true y
values with the predicted ones.
Methods description
-
sklearn.metrics
: This module from scikit-learn (sklearn) contains various metrics for evaluating machine learning models. It provides functions to assess the performance of classification, regression, and clustering algorithms; -
accuracy_score
: This is a method within themetrics
module used to calculate the accuracy of classification models. It compares the predicted labels (y_pred
) with the true labels (y_test
) and returns the ratio of correctly classified samples to the total number of samples. In other words, it measures the proportion of correctly predicted outcomes.
Swipe to start coding
-
Import
accuracy_score
fromsklearn.metrics
. -
Pass
y_test
andy_pred
to the scoring function.
Lösung
Congratulations on building your first ML Pipeline. In this project, you have learned how to:
- Import your Data;
- Visually inspect it;
- Preprocess it (clean and impute it);
- Train and evaluate your ML algorithm.
Danke für Ihr Feedback!