ML | Log Loss and Mean Squared Error Last Updated : 07 Aug, 2024 Comments Improve Suggest changes Like Article Like Report Log Loss It is the evaluation measure to check the performance of the classification model. It measures the amount of divergence of predicted probability with the actual label. So lesser the log loss value, more the perfectness of model. For a perfect model, log loss value = 0. For instance, as accuracy is the count of correct predictions i.e. the prediction that matches the actual label, Log Loss value is the measure of uncertainty of our predicted labels based on how it varies from the actual label. where, N : no. of samples.M : no. of attributes.yij : indicates whether ith sample belongs to jth class or not.pij : indicates probability of ith sample belonging to jth class.Implementation of LogLoss using sklearn Python3 1== from sklearn.metrics import log_loss: LogLoss = log_loss(y_true, y_pred, eps = 1e-15, normalize = True, sample_weight = None, labels = None) Mean Squared Error It is simply the average of the square of the difference between the original values and the predicted values. Implementation of Mean Squared Error using sklearn Python3 1== from sklearn.metrics import mean_squared_error MSE = mean_squared_error(y_true, y_pred) Comment More infoAdvertise with us Next Article ML | Log Loss and Mean Squared Error mohit gupta_omg :) Follow Improve Article Tags : Machine Learning AI-ML-DS AI-ML-DS With Python Practice Tags : Machine Learning Similar Reads ML | Models Score and Error In Machine Learning one of the main tasks is to model the data and predict the output using various Classification and Regression Algorithms. But since there are so many Algorithms, it is really difficult to choose the one for predicting the final data. So we need to compare our models and choose th 2 min read ML | Mathematical explanation of RMSE and R-squared error RMSE: Root Mean Square Error is the measure of how well a regression line fits the data points. RMSE can also be construed as Standard Deviation in the residuals. Consider the given data points: (1, 1), (2, 2), (2, 3), (3, 6). Let us break the above data points into 1-d lists. Input: x = [1, 2, 5 min read Least Mean-Squares Algorithm in Neural Networks The Least Mean-Squares (LMS) algorithm is a widely used adaptive filter technique in neural networks, signal processing, and control systems. Developed by Bernard Widrow and Ted Hoff in 1960, the LMS algorithm is a stochastic gradient descent method that iteratively updates filter coefficients to mi 10 min read Least Mean Squares Filter in Signal Processing Filtering is a fundamental process in signal processing used to enhance or extract useful information from a signal by reducing noise, isolating certain frequency components, or performing other transformations. Filters are employed in various applications such as audio processing, image enhancement 6 min read Training and Validation Loss in Deep Learning In deep learning, loss functions are crucial in guiding the optimization process. The loss represents the discrepancy between the predicted output of the model and the actual target value. During training, models attempt to minimize this loss by adjusting their weights. Training loss and validation 6 min read Like