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python sklearn confusion matrix

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YOU.com answered on April 24, 2020 Popularity 9/10 Helpfulness 1/10

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python sklearn confusion matrix

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from sklearn.metrics import confusion_matrix

# Ground truth values
y_true = [2, 0, 2, 2, 0, 1]
# Predicted values
y_pred = [0, 0, 2, 2, 0, 2]

# Compute the confusion matrix
cm = confusion_matrix(y_true, y_pred)

print(cm)

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Contributed on Sep 12 2023

YOU.com

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confusion matrix python

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from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_mat, square=True, annot=True, cmap='Blues', fmt='d', cbar=False)

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Contributed on Nov 23 2021

Adventurous Addax

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sklearn plot confusion matrix

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import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, plot_confusion_matrix

clf = # define your classifier (Decision Tree, Random Forest etc.)
clf.fit(X, y) # fit your classifier

# make predictions with your classifier
y_pred = clf.predict(X)         
# optional: get true negative (tn), false positive (fp)
# false negative (fn) and true positive (tp) from confusion matrix
M = confusion_matrix(y, y_pred)
tn, fp, fn, tp = M.ravel() 
# plotting the confusion matrix
plot_confusion_matrix(clf, X, y)
plt.show()

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Contributed on May 14 2021

wolf-like_hunter

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confusion matrix python

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By definition, entry i,j in a confusion matrix is the number of 
observations actually in group i, but predicted to be in group j. 
Scikit-Learn provides a confusion_matrix function:

from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
#        [0, 1, 2],
#        [2, 1, 3]], dtype=int64)

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Source: scikit-learn.org

Tags: confusion-matrix conf

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Contributed on Apr 24 2020

Bored Coder

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confusion matrix python

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from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report, confusion_matrix

print(confusion_matrix(y_test, y_pred_test.round()))
print(classification_report(y_test, y_pred_test.round()))

# Output:
[[99450   250]
 [ 4165 11192]]
              precision    recall  f1-score   support

           0       0.96      1.00      0.98     99700
           1       0.98      0.73      0.84     15357

    accuracy                           0.96    115057
   macro avg       0.97      0.86      0.91    115057
weighted avg       0.96      0.96      0.96    115057

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Contributed on Apr 19 2022

Ruben Visser

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import sklearn.metrics from plot_confusion_matrix

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from sklearn.metrics import plot_confusion_matrix

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Contributed on Oct 17 2020

sbmthakur

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sklearn plot confusion matrix

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from sklearn.linear_model import LogisticRegression
from sklearn.metrics import  plot_confusion_matrix
clf = LogisticRegression()
clf.fit(X_train,y_train)
disp = plot_confusion_matrix(clf,X_test,y_test,cmap="Blues",values_format='.3g')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

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Contributed on Jun 01 2022

Thankful Tuatara

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confusion_matrix sklearn plot

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import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

# Sample true and predicted labels
true_labels = np.array([0, 0, 1, 1, 2, 2])
predicted_labels = np.array([0, 0, 1, 1, 1, 2])

# Compute confusion matrix
cm = confusion_matrix(true_labels, predicted_labels)

# Define class labels
class_labels = ['Class 0', 'Class 1', 'Class 2']

# Plot confusion matrix
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(len(class_labels))
plt.xticks(tick_marks, class_labels, rotation=45)
plt.yticks(tick_marks, class_labels)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')

# Fill confusion matrix cells with values
thresh = cm.max() / 2.
for i, j in np.ndindex(cm.shape):
    plt.text(j, i, format(cm[i, j], 'd'), horizontalalignment="center",
             color="white" if cm[i, j] > thresh else "black")

plt.show()

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Contributed on Sep 12 2023

YOU.com

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how to get confusion matrix in python

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from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)

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Contributed on Dec 23 2021

Colorful Copperhead

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plot confusion matrix scikit learn

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-1

from sklearn import metrics
metrics.ConfusionMatrixDisplay.from_predictions(true_y, predicted_y).plot()

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Contributed on Apr 06 2022

Arno Deceuninck

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confusion matrix python

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df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)

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Source: stackoverflow.com

Tags: confusion-matrix conf

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Contributed on Jan 18 2021

Bad Bison

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python sklearn confusion matrix

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confusion matrix python

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import sklearn.metrics from plot_confusion_matrix

sklearn plot confusion matrix

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plot confusion matrix scikit learn

confusion matrix python

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