X, y = make_blobs(n_samples=5000, centers=3, n_features=2, cluster_std=2, random_state=2) These are similar to binary classification cross-entropy, used for multi-class classification problems. If we take a dataset like Iris where we need to predict the three-class labels: Setosa, Versicolor and Virginia, in such cases where the target variable has more than two classes Multi-Class Classification Loss function is used. Pyplot.show() Multi-Class Classification Loss Function pile(loss='hinge', optimizer=opt, metrics=) These are particularly used in SVM models. It penalizes the model when there is a difference in the sign between the actual and predicted class values. This type of loss is used when the target variable has 1 or -1 as class labels. Pyplot.title('Binary Cross Entropy Loss') Test_acc = model.evaluate(testX, testy, verbose=0) Train_acc = model.evaluate(trainX, trainy, verbose=0) pile(loss='binary_crossentropy', optimizer=opt, metrics=) Model.add(Dense(1, activation='sigmoid')) Model.add(Dense(50, input_dim=2, activation='relu', kernel_initializer='he_uniform')) # Cross entropy lossįrom sklearn.datasets import make_circles Cross-Entropy calculates the average difference between the predicted and actual probabilities. ![]() It gives the probability value between 0 and 1 for a classification task. Suppose we are dealing with a Yes/No situation like “a person has diabetes or not”, in this kind of scenario Binary Classification Loss Function is used. Pyplot.show() Binary Classification Loss Function ![]() pile(loss='mean_absolute_error', optimizer=opt, metrics=) Sometimes there may be some data points which far away from rest of the points i.e outliers, in case of cases Mean Absolute Error Loss will be appropriate to use as it calculates the average of the absolute difference between the actual and predicted values. Pyplot.title('Mean Squared Logarithmic Error Loss') pile(loss='mean_squared_logarithmic_error', optimizer=opt, metrics=) The model will now penalize less in comparison to the earlier method. This will overcome the problem possessed by the Mean Square Error Method. Suppose we want to reduce the difference between the actual and predicted variable we can take the natural logarithm of the predicted variable then take the mean squared error. Pyplot.show() 2.Mean Squared Logarithmic Error Loss Test_mse = model.evaluate(testX, testy, verbose=0) Train_mse = model.evaluate(trainX, trainy, verbose=0) History = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=50, verbose=0) pile(loss='mean_squared_error', optimizer=opt) Model.add(Dense(25, input_dim=20, activation='relu', kernel_initializer='he_uniform')) ![]() Y = StandardScaler().fit_transform(y.reshape(len(y),1)) X, y = make_regression(n_samples=5000, n_features=20, noise=0.1, random_state=1) Practical Implementation from sklearn.datasets import make_regressionįrom sklearn.preprocessing import StandardScaler If the difference is large the model will penalize it as we are computing the squared difference. Mean Squared Error is the mean of squared differences between the actual and predicted value. Regression Loss is used when we are predicting continuous values like the price of a house or sales of a company.
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