Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Model Evaluation | Logistic Regression Mastering
Logistic Regression Mastering

# 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 the `metrics` 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.

Task

1. Import `accuracy_score` from `sklearn.metrics`.
2. Pass `y_test` and `y_pred` to the scoring function.

Congratulations on building your first ML Pipeline. In this project, you have learned how to:

1. Import your Data;
2. Visually inspect it;
3. Preprocess it (clean and impute it);
4. Train and evaluate your ML algorithm.

Everything was clear?

Section 1. Chapter 7

Course Content

Logistic Regression Mastering

# 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 the `metrics` 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.

Task

1. Import `accuracy_score` from `sklearn.metrics`.
2. Pass `y_test` and `y_pred` to the scoring function.

Congratulations on building your first ML Pipeline. In this project, you have learned how to:

1. Import your Data;
2. Visually inspect it;
3. Preprocess it (clean and impute it);
4. Train and evaluate your ML algorithm.

Everything was clear?

Section 1. Chapter 7