mean_squared_error Mean squared error regression loss mean_squared_log_error (or aliases: msle, MSLE) Mean squared logarithmic error regression loss root_mean_squared_log_error (or aliases: rmsle, RMSLE) Root mean squared logarithmic error regression loss root_mean_squared_error Root mean squared...
result = np.linalg.norm(v): This line computes the 2-norm (also known as the Euclidean norm) of the vector v. The 2-norm is the square root of the sum of the squared elements of the vector. In this case, the result is approximately 11.832159566199232. m = np.matrix('1, 2; 3, ...
The quality of the models was gauged based on well-known evaluation metrics such as the accuracy of the classification, precision, recall, and F1-scores for classification and the Mean Absolute Error (MAE), R squared score(R2), and Mean Squared Error (MSE) for regression. Evaluation metrics ...
Trimmed means are robust estimators of central tendency. To compute a trimmed mean, we remove a predetermined amount of observations on each side of a distribution, and average the remaining observations. ... Indeed, themedian is an extreme trimmed mean, in which all observations are removed exc...
(loss=keras.losses.categorical_crossentropy,# model.compile(loss=keras.losses.mean_squared_error,# model.compile(loss=categorical_bernoulli_crossentropy,# approxmodel.compile(loss=[categorical_crossentropy,categorical_crossentropy],loss_weights=[0.5,0.5],optimizer=keras.optimizers.Adadelta(),metrics=['...
Why do some regressions models give you r and some give you r squared? What does r squared tell you? If you take the square root of r squared for a model and get a number close to one does that mean that the closer the square root...