# 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

- Import
`accuracy_score`

from`sklearn.metrics`

. - 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:

- Import your Data;
- Visually inspect it;
- Preprocess it (clean and impute it);
- Train and evaluate your ML algorithm.

Everything was clear?

Section 1. Chapter 7

Course Content

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

- Import
`accuracy_score`

from`sklearn.metrics`

. - 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:

- Import your Data;
- Visually inspect it;
- Preprocess it (clean and impute it);
- Train and evaluate your ML algorithm.

Everything was clear?

Section 1. Chapter 7