When using classification models in machine learning, there are three common metrics that we use to assess the quality of the model:

**1. Precision**: Percentage of correct positive predictions relative to total positive predictions.

**2. Recall**: Percentage of correct positive predictions relative to total actual positives.

**3. F1 Score**: A weighted harmonic mean of precision and recall. The closer to 1, the better the model.

- F1 Score: 2 * (Precision * Recall) / (Precision + Recall)

Using these three metrics, we can understand how well a given classification model is able to predict the outcomes for some response variable.

Fortunately, when fitting a classification model in Python we can use the **classification_report()** function from the **sklearn** library to generate all three of these metrics.

The following example shows how to use this function in practice.

**Example: How to Use the Classification Report in sklearn**

For this example, we’ll fit a logistic regression model that uses points and assists to predict whether or not 1,000 different college basketball players get drafted into the NBA.

First, we’ll import the necessary packages to perform logistic regression in Python:

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report

Next, we’ll create the data frame that contains the information on 1,000 basketball players:

**#make this example reproducible
np.random.seed(1)
#create DataFrame
df = pd.DataFrame({'points': np.random.randint(30, size=1000),
'assists': np.random.randint(12, size=1000),
'drafted': np.random.randint(2, size=1000)})
#view DataFrame
df.head()
points assists drafted
0 5 1 1
1 11 8 0
2 12 4 1
3 8 7 0
4 9 0 0
**

**Note**: A value of **0** indicates that a player did not get drafted while a value of **1** indicates that a player did get drafted.

Next, we’ll split our data into a training set and testing set and fit the logistic regression model:

#define the predictor variables and the response variable X = df[['points', 'assists']] y = df['drafted'] #split the dataset into training (70%) and testing (30%) sets X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)#instantiate the model logistic_regression = LogisticRegression() #fit the model using the training data logistic_regression.fit(X_train,y_train) #use model to make predictions on test data y_pred = logistic_regression.predict(X_test)

Lastly, we’ll use the **classification_report()** function to print the classification metrics for our model:

#print classification report for model print(classification_report(y_test, y_pred)) precision recall f1-score support 0 0.51 0.58 0.54 160 1 0.43 0.36 0.40 140 accuracy 0.48 300 macro avg 0.47 0.47 0.47 300 weighted avg 0.47 0.48 0.47 300

Here’s how to interpret the output:

**Precision**: Out of all the players that the model predicted would get drafted, only **43%** actually did.

**Recall**: Out of all the players that actually did get drafted, the model only predicted this outcome correctly for **36%** of those players.

**F1 Score**: This value is calculated as:

- F1 Score: 2 * (Precision * Recall) / (Precision + Recall)
- F1 Score: 2 * (.43 * .36) / (.43 + .36)
- F1 Score:
**0.40**.

Since this value isn’t very close to 1, it tells us that the model does a poor job of predicting whether or not players will get drafted.

**Support**: These values simply tell us how many players belonged to each class in the test dataset. We can see that among the players in the test dataset, **160** did not get drafted and **140** did get drafted.

**Note**: You can find the complete documentation for the **classification_report()** function here.

**Additional Resources**

The following tutorials provide additional information on how to use classification models in Python:

How to Perform Logistic Regression in Python

How to Create a Confusion Matrix in Python

How to Calculate Balanced Accuracy in Python