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.
The model predicted true and it is true.
The model predicted that someone is sick and the person is sick.
The model predicted false and it is false.
The model predicted that someone is not sick and the person is not sick.
The model predicted True and it is false.
The model predicted that someone is sick and the person is not sick.
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:
- Run a classification algorithm
y_pred = classifier.predict(X_test)
- Import metrics from the sklearn module
from sklearn.metrics import confusion_matrix
- Run the confusion matrix function on actual and predicted values
- Plot the confusion matrix
plot_confusion_matrix(classifier, X_test, y_test, cmap=plt.cm.Blues)
- Inspect the classification report
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.datasets import load_breast_cancer 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] # Load data dataset = load_breast_cancer() 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.
Create a confusion matrix
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]])
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||Quality measurement of predictions|
|Scikit-learn||Machine learning package in Python|
|True positive||Model correctly predicts the positive class|
|False positive||Model incorrectly predicts the positive class|
|True negative||Model correctly predicts the negative class|
|False negative||Model incorrectly predicts the negative class|
|Install sklearn||pip install -U scikit-learn|
|Python library import||from sklearn.metrics import confusion_matrix|
|Plot confusion matrix||sklearn.metrics.plot_confusion_matrix|
Confusion Matrix FAQs
Run a classification algorithm, import the confusion matrix function from the sklearn.metrics module, run function on test and prediction and plot the matrix.
use the confusion matrix to evaluate the performance of a machine learning classification algorithm.
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.
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.
The confusion matrix is used to evaluate the accuracy of a machine learning model that tries to predict classes (e.g. Classification).
Use the confusion_matrix function from the sklearn.metrics module.
No. Confusion matrix can be used for binary classification as well as multi-class classification problems.
This article was quite big to grasp.
All I want you to leave with is that it is super important to look at the confusion matrix to help you fine-tune your machine learning models.
This can modify the accuracy score quite heavily in some cases.
Good work on building your first confusion matrix in Scikit-learn.
SEO Strategist at Tripadvisor, ex- Seek (Melbourne, Australia). Specialized in technical SEO. In a quest to programmatic SEO for large organizations through the use of Python, R and machine learning.