# How to use Confusion Matrix in Scikit-Learn (with Example)

The confusion matrix is often used in machine learning to compute the accuracy of a classification algorithm.

It can be used in binary classifications as well as multi-class classification problems.

## What the Confusion Matrix Measures?

It measures the quality of predictions from a classification model by looking at how many predictions are True and how many are False.

Specifically, it computes:

• True positives (TP)
• False positives (FP)
• True negatives (TN)
• False negatives (FN)

## Understand the Confusion Matrix

Here, we will try to make sense of the true positive, true negative, false positive and false negative values mean.

### True Positive

The model predicted true and it is true.

The model predicted that someone is sick and the person is sick.

### True Negative

The model predicted false and it is false.

The model predicted that someone is not sick and the person is not sick.

### False Positive

The model predicted True and it is false.

The model predicted that someone is sick and the person is not sick.

### False Negative

The model predicted false and it is true.

The model predicted that someone is not sick and the person is sick.

## How to Create a Confusion Matrix in Scikit-learn?

In order to get a confusion matrix in scikit-learn:

1. Run a classification algorithm

classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test) 2. Import metrics from the sklearn module

from sklearn.metrics import confusion_matrix 3. Run the confusion matrix function on actual and predicted values

confusion_matrix(y_test, y_pred) 4. Plot the confusion matrix

plot_confusion_matrix(classifier, X_test, y_test, cmap=plt.cm.Blues)
plt.show() 5. Inspect the classification report

print(classification_report(y_test, y_pred)) ### Run a classification algorithm

In a previous article, we classified breast cancers using the k-nearest neighbors algorithm from scikit-learn.

I will not explain this part of the code, but you can look at the detail in the article on the k-nearest neighbors.

```import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

def to_target(x):
"""Map targets to target names"""
return list(dataset.target_names)[x]

df = pd.DataFrame(dataset.data,columns=dataset.feature_names)
df['target'] = pd.Series(dataset.target)
df['target_names'] = df['target'].apply(to_target)

# Define predictor and predicted datasets
X = df.drop(['target','target_names'], axis=1).values
y = df['target_names'].values

# split taining and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)

# train the model
knn = KNeighborsClassifier(n_neighbors=8)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

# compute accuracy of the model
knn.score(X_test, y_test)
```

The result is an accuracy score of the model.

```0.9239766081871345
```

### Create a confusion matrix

Use the `confusion_matrix` method from `sklearn.metrics` to compute the confusion matrix.

```from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test,y_pred)
cm
```

The result is an array in which positions are the same as the quadrant we saw in the past.

```array([[ 57,   7],
[  5, 102]])
```
• `cm` = TP
• `cm` = TN
• `cm` = FP
• `cm` = FN

### Plot the confusion matrix

You can use the `plot_confusion_matrix` method to visualize the confusion matrix.

```import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix

color = 'white'
matrix = plot_confusion_matrix(knn, X_test, y_test, cmap=plt.cm.Blues)
matrix.ax_.set_title('Confusion Matrix', color=color)
plt.xlabel('Predicted Label', color=color)
plt.ylabel('True Label', color=color)
plt.gcf().axes.tick_params(colors=color)
plt.gcf().axes.tick_params(colors=color)
plt.show()
```

The result is your confusion matrix plot.

• Top left quadrant = True Positives = Number of benign labelled as benign
• Bottom right quadrant = True Negatives = Number of malignant labelled as malignant
• Top right quadrant = False Positives = Number of benign labelled as malignant
• Bottom left quadrant = False Negatives = Number of malignant labelled as benign

### Run the classification report

With data from the confusion matrix, you can interpret the results by looking at the classification report.

```from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))
```

If you don’t understand the result above, make sure that you read the article that I wrote on the classification report.

## Confusion Matrix FAQs

How do you get a confusion matrix in scikit-learn?

Run a classification algorithm, import the confusion matrix function from the sklearn.metrics module, run function on test and prediction and plot the matrix.

Why use confusion matrix?

use the confusion matrix to evaluate the performance of a machine learning classification algorithm.

Is confusion matrix better than accuracy?

The confusion matrix provides more insights into a model’s performance than classification accuracy as it shows the number of correctly and incorrectly classified instances.

Can confusion matrix be used on continuous values (e.g. linear regression)?

Confusion matrices shows the accuracy of the prediction of classes. When trying to predict a number output like in the case of the continuous output of a regression model, confusion matrix should not be used.

What is confusion matrix used for?

The confusion matrix is used to evaluate the accuracy of a machine learning model that tries to predict classes (e.g. Classification).

How do you create a confusion matrix in Python?

Use the confusion_matrix function from the sklearn.metrics module.

Is confusion matrix only for binary classification problems?

No. Confusion matrix can be used for binary classification as well as multi-class classification problems.