Loss is basically a penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; othe...

Bias and Variance are prediction errors when it comes to accuracy in any machine learning algorithm. Bias refers to how well your model can represent all possible outcomes, whereas variance refers to how...

Learning curves in machine learning are used to evaluate how models will perform with varying numbers of training samples. This is achieved by monitoring training and validation scores (model accuracy) w...

It is sometimes helpful to plot the influence of a single hyperparameter on the training score and the validation score to find out whether the estimator is overfitting or underfitting for some hyperpara...

Classification is a task of machine learning which assigns a label value to a specific observation and then uses these labelled observations to identify a particular type to be of one kind or the other. ...

Logistic regression takes some inputs and calculates the probability of some outcome. For example, if a child has a temperature of 104F (40C) and they have a rash and nausea then the probability that the...