Accuracy Score Valueerror: Can't Handle Mix of Binary and Continuous Target

Classification metrics can't handle a mix of binary and continuous targets

All your commented-out models are not classifiers but regression models, for which accuracy is meaningless.

You get the error because these regression models do not produce binary outcomes, but continuous (float) numbers (as all regression models do); so, when scikit-learn attempts to calculate the accuracy by comparing a binary number (true label) with a float (predicted value), it not unexpectedly gives an error. And this cause is clearly hinted at the error message itself:

Classification metrics can't handle a mix of binary and continuous target

Confusion Matrix ValueError: Classification metrics can't handle a mix of binary and continuous targets

The model outputs the predicted probabilities, you need to transform them back to class labels before calculating the classification metrics, see below.

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
tf.random.set_seed(0)

# generate the data
X, y = make_classification(n_classes=2, n_features=4, n_informative=4, n_redundant=0, random_state=42)

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# build the model
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(4,)))
model.add(Dropout(0.1))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(16, input_dim=1, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(12, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(1, activation='sigmoid'))

# fit the model
model.compile(loss='binary_crossentropy', optimizer='RMSprop', metrics=['accuracy', 'AUC'])
model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test), validation_batch_size=64, verbose=0)

# extract the predicted probabilities
p_pred = model.predict(X_test)
p_pred = p_pred.flatten()
print(p_pred.round(2))
# [1. 0.01 0.91 0.87 0.06 0.95 0.24 0.58 0.78 ...

# extract the predicted class labels
y_pred = np.where(p_pred > 0.5, 1, 0)
print(y_pred)
# [1 0 1 1 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 ...

print(confusion_matrix(y_test, y_pred))
# [[13  1]
#  [ 2  9]]

print(classification_report(y_test, y_pred))
#               precision    recall  f1-score   support
#            0       0.87      0.93      0.90        14
#            1       0.90      0.82      0.86        11
#     accuracy                           0.88        25
#    macro avg       0.88      0.87      0.88        25
# weighted avg       0.88      0.88      0.88        25

ValueError: "metrics can't handle a mix of binary and continuous targets" with no source

You are trying to use a regression algorithm (DecisionTreeRegressor) for a binary classification problem; the regression model, as expected, gives continuous outputs, but the accuracy_score, where the error actually happens:

File "C:/Users/Omar/Downloads/Kaggle Competition/Titanic.py", line 58, in <module>
    print(accuracy_score(val_y, val_predictions)) 

expects binary ones, hence the error.

For starters, change your model to

from sklearn.tree import DecisionTreeClassifier

titanic_model = DecisionTreeClassifier()

Python Sklearn "ValueError: Classification metrics can't handle a mix of multiclass-multioutput and binary targets" error

You probably want to get the accuracy for the predictions together with the column Survived of the test_survived dataframe:

from sklearn.metrics import  accuracy_score
accuracy=accuracy_score(test_survived['Survived'],predictions)
print(accuracy)

Your error occured, because the accuracy_score() only takes two 1-dimensional arrays, one as the ground truth labels and the other as the predicted labels. But you provided a 2-dimensional "array" (the dataframe) and the 1-dimensional predictions, hence it assumed that your first input is a multiclass-output.

The documentation is also very resourceful for this.

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