In this Python tutorial, we will learn **How Scikit learn confusion matrix works** in Python and we will also cover different examples related to **Scikit learn confusion matrix**. And, we will cover these topics.

- Scikit learn confusion matrix
- Scikit learn confusion matrix example
- Scikit learn confusion matrix plot
- Scikit learn confusion matrix accuracy
- Scikit learn confusion matrix multiclass
- Scikit learn confusion matrix display
- Scikit learn confusion matrix labels
- Scikit learn confusion matrix normalize

## Scikit learn confusion matrix

In this section, we will learn about **how the Scikit learn confusion matrix works** in python.

- Scikit learn confusion matrix is defined as a technique to calculate the performance of classification.
- The confusion matrix is also used to predict or summarise the result of the classification problem.

**Code:**

**y_true = [2, 0, 0, 2, 0, 1]**is used to get the true value.**y_pred = [0, 0, 2, 0, 0, 2]**is used to get the predicted value.**confusion_matrix(y_true, y_pred)**is used to evaluate the confusion matrix.

```
from sklearn.metrics import confusion_matrix
y_true = [2, 0, 0, 2, 0, 1]
y_pred = [0, 0, 2, 0, 0, 2]
confusion_matrix(y_true, y_pred)
```

**Output:**

After running the above code, we get the following output in which we can see that the confusion matrix value is printed on the screen.

Read: Scikit learn Image Processing

## Scikit learn confusion matrix example

In this section, we will learn about **how Scikit learn confusion matrix example** works in python.

- Scikit learn confusion matrix example is defined as a technique to summarise the result of the classification.
- The confusion matrix also predicted the number of correct and incorrect predictions of the classification model.

**Code:**

In the following code, we will import some libraries from which we can make the confusion matrix.

**iris = datasets.load_iris()**is used to load the iris data.**class_names = iris.target_names**is used to get the target names.**x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)**is used to split the data set into train and test data.**classifier = svm.SVC(kernel=”linear”, C=0.02).fit(x_train, y_train)**is used to fit the model.**ConfusionMatrixDisplay.from_estimator()**is used to plot the confusion matrix.**print(display.confusion_matrix)**is used to print the confusion matrix.

```
import numpy as num
import matplotlib.pyplot as plot
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import ConfusionMatrixDisplay
iris = datasets.load_iris()
x = iris.data
y = iris.target
class_names = iris.target_names
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)
classifier = svm.SVC(kernel="linear", C=0.02).fit(x_train, y_train)
num.set_printoptions(precision=2)
title_options = [
("Confusion matrix, without normalization", None),
("Normalized confusion matrix", "true"),
]
for title, normalize in title_options:
display = ConfusionMatrixDisplay.from_estimator(
classifier,
x_test,
y_test,
display_labels=class_names,
cmap=plot.cm.Blues,
normalize=normalize,
)
display.ax_.set_title(title)
print(title)
print(display.confusion_matrix)
plot.show()
```

**Output:**

In the following output, we can see that the result of the classification is summarised on the screen with help of a confusion matrix.

Read: Scikit learn non-linear [Complete Guide]

## Scikit learn confusion matrix plot

In this section, we will learn about **how Scikit learn confusion matrix plot** in python.

- Scikit learn confusion matrix plot is used to plot the graph on the screen to summarise the result of the model.
- It is used to plot the graph to predict the number of correct or incorrect predictions of the model.

**Code:**

In the following code, we will import some libraries from which we can plot the confusion matrix on the screen.

**x, y = make_classification(random_state=0)**is used to make classification.**x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)**is used to split the data set into train and test set.**classifier.fit(x_train, y_train)**is used to fit the model.**plot_confusion_matrix(classifier, x_test, y_test)**is used to plot the confusion matrix on the screen.

```
import matplotlib.pyplot as plot
from sklearn.datasets import make_classification
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
x, y = make_classification(random_state=0)
x_train, x_test, y_train, y_test = train_test_split(
x, y, random_state=0)
classifier = SVC(random_state=0)
classifier.fit(x_train, y_train)
SVC(random_state=0)
plot_confusion_matrix(classifier, x_test, y_test)
plot.show()
```

**Output:**

After running the above code, we get the following output in which we can see that the confusion matrix is plotted on the screen.

Read: Scikit learn KNN Tutorial

## Scikit learn confusion matrix accuracy

In this section, we will learn about **Scikit learn confusion matrix accuracy** of the model in python.

Scikit learn confusion matrix accuracy is used to calculate the accuracy of the matrix how accurate our model result.

**Code:**

In the following code, we will import some libraries from which we can calculate the accuracy of the model.

**y_pred = [1, 3, 2, 0]**is used to predict the predicted value.**y_true = [0, 1, 2, 3]**is used to predict the true value.**accuracy_score(y_true, y_pred)**is used to predict the accuracy score.

```
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
y_pred = [1, 3, 2, 0]
y_true = [0, 1, 2, 3]
accuracy_score(y_true, y_pred)
accuracy_score(y_true, y_pred, normalize=False)
import numpy as num
accuracy_score(num.array([[0, 1], [1, 1]]), num.ones((2, 2)))
```

**Output:**

After running the above code, we get the following output in which we can see that the confusion matrix accuracy score is printed on the screen.

Read: Scikit learn Sentiment Analysis

## Scikit learn confusion matrix multiclass

In this section, we will learn about **how scikit learn confusion matrix multiclass works** in python.

Scikit learn confusion matrix multi-class is defined as a problem of classifying illustration of one of the three or more classes.

**Code:**

In the following code, we will import some libraries from which we can make a confusion matrix multiclass.

**df = pd.read_csv(“IRIS.csv”)**is used to load the dataset.**df.dtypes**is used to select the types of data.

**#importing packages**
import pandas as pds
import numpy as num
import seaborn as sb
import matplotlib.pyplot as plot
df = pd.read_csv("IRIS.csv")
df.head()
df.dtypes

**x = df.drop([‘species’], axis=1)**is used to separating independent variable and dependent variable.**print(x.shape)**is used to print the shape of the dataset.

**#Separating independant variable and dependent variable("Species")**
x = df.drop(['species'], axis=1)
y = df['species']
print(x.shape)
print(y.shape)

**x_train, x_test, y_train, y_test = train_test_split(x, y, test_**size=**0.3, random_state=0)**is used to split the dataset into train and test set.**print(x_train.shape)**is used to print the shape of the train set.**print(x_test.shape)**is used to print the shape of the test set.

**# Splitting the dataset to Train and test**
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

**classifier = SVC(kernel = ‘linear’).fit(x_train,y_train)**is used to training the classifier from x train and y train.**cm = confusion_matrix(y_test, y_pred)**is used to creating the confusion matrix.**cm_df = pd.DataFrame()**is used to create the data frame.**plot.figure(figsize=(5,4))**is used to plot the figure.

```
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
classifier = SVC(kernel = 'linear').fit(x_train,y_train)
classifier.predict(x_train)
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
cm_df = pd.DataFrame(cm,
index = ['SETOSA','VERSICOLR','VIRGINICA'],
columns = ['SETOSA','VERSICOLR','VIRGINICA'])
plot.figure(figsize=(5,4))
sb.heatmap(cm_df, annot=True)
plot.title('Confusion Matrix')
plot.ylabel('Actal Values')
plot.xlabel('Predicted Values')
plot.show()
```

Read: Scikit learn Gradient Descent

## Scikit learn confusion matrix display

In this section, we will learn about how **Scikit learn confusion matrix display works** in python.

Scikit learn confusion matrix display is defined as a matrix in which i,j is equal to the number of observations are forecast to be in a group.

**Code:**

In the following code, we will learn to import some libraries from which we can see how the confusion matrix is displayed on the screen.

**x, y = make_classification(random_state=0)**is used to make classification.**x_train, x_test, y_train, y_test = train_test_split(x, y,random_state=0)**is used to split the dataset into train and test data.**classifier.fit(x_train, y_train)**is used to fit the data.- predictions = classifier.predict(x_test) is used to predict the data.
**display=ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=classifier.classes_)**is used to display the confusion matrix.**display.plot()**is used to plot the matrix.

```
import matplotlib.pyplot as plot
from sklearn.datasets import make_classification
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
x, y = make_classification(random_state=0)
x_train, x_test, y_train, y_test = train_test_split(x, y,
random_state=0)
classifier = SVC(random_state=0)
classifier.fit(x_train, y_train)
SVC(random_state=0)
predictions = classifier.predict(x_test)
cm = confusion_matrix(y_test, predictions, labels=classifier.classes_)
display = ConfusionMatrixDisplay(confusion_matrix=cm,
display_labels=classifier.classes_)
display.plot()
plot.show()
```

**Output:**

After running the above code, we get the following output in which we can see that a confusion matrix is displayed on the screen.

Read: Scikit learn Classification Tutorial

## Scikit learn confusion matrix labels

In this section, we will learn **how Scikit learn confusion matrix labels works in **python.

Scikit learn confusion matrix label is defined as a two-dimension array that contrasts a predicted group of labels with true labels.

**Code:**

In the following code, we will import some libraries to know how scikit learn confusion matrix labels works.

**y_true = num.array([[1, 0, 0],[0, 1, 1]])**is used to collect the true labels in the array.**y_pred = num.array([[1, 0, 1],[0, 1, 0]])**is used to collect the predicted labelsin the array.**multilabel_confusion_matrix(y_true, y_pred)**is used to get multilabel confusion matrix.

```
import numpy as num
from sklearn.metrics import multilabel_confusion_matrix
y_true = num.array([[1, 0, 0],
[0, 1, 1]])
y_pred = num.array([[1, 0, 1],
[0, 1, 0]])
multilabel_confusion_matrix(y_true, y_pred)
```

**Output:**

After running the above code, we get the following output in which we can see that the confusion matrix labels are printed on the screen.

Read: Scikit learn Hyperparameter Tuning

## Scikit learn confusion matrix normalize

In this section, we will learn about **how scikit learn confusion matrix normalize works **in python.

Scikit learn confusion matrix normalize is defined as a process that represents one sample is present in each group.

**Code:**

In the following code, we will import some libraries from which we can normalize the matrix.

**iris = datasets.load_iris()**is used to load the data.**x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0)**is used use to split the dataset into train and test data.**classifier = svm.SVC(kernel=’linear’, C=0.01)**is used as a classifier.**y_pred = classifier.fit(x_train, y_train).predict(x_test)**is used to fit the model.**cm = confusion_matrix(y_true, y_pred)**is used to compute the confusion matrix.**classes = classes[unique_labels(y_true, y_pred)]**is used of label that appear in the data.**figure, axis = plot.subplots()**is used to plot the figure on the screen.-
**plot.setp(axis.get_xticklabels(), rotation=45, ha=”right”,rotation_mode=”anchor”)**is used to set the alignment and rotate the ticks. -
**plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True, title=’Normalized confusion matrix’)**is used to plot the normalized confusion matrix.

```
import numpy as num
import matplotlib.pyplot as plot
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
iris = datasets.load_iris()
x_digits = iris.data
y = iris.target
class_names = iris.target_names
x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0)
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(x_train, y_train).predict(x_test)
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plot.cm.Blues):
if not title:
if normalize:
title = 'Normalized confusion matrix'
cm = confusion_matrix(y_true, y_pred)
classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, num.newaxis]
print("Normalized confusion matrix")
print(cm)
figure, axis = plot.subplots()
im = axis.imshow(cm, interpolation='nearest', cmap=cmap)
axis.figure.colorbar(im, ax=axis)
axis.set(xticks=num.arange(cm.shape[1]),
yticks=num.arange(cm.shape[0]),
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
plot.setp(axis.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
axis.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="cyan" if cm[i, j] > thresh else "red")
figure.tight_layout()
return axis
plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
title='Normalized confusion matrix')
plot.show()
```

**Output:**

In the following code, we will see a normalized confusion matrix array is created, and also a normalized confusion matrix graph is plotted on the screen.

Also, take a look at some more Scikit learn tutorials.

- Scikit learn Linear Regression
- Scikit learn Feature Selection
- Scikit learn Ridge Regression
- Scikit learn Random Forest

So, in this tutorial we discussed **Scikit learn confusion matrix** and we have also covered different examples related to its implementation. Here is the list of examples that we have covered.

- Scikit learn confusion matrix
- Scikit learn confusion matrix example
- Scikit learn confusion matrix plot
- Scikit learn confusion matrix accuracy
- Scikit learn confusion matrix multiclass
- Scikit learn confusion matrix display
- Scikit learn confusion matrix labels
- Scikit learn confusion matrix normalize

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