Two crucial metrics to consider when evaluating your predictions are variance and bias. The degree by which the approximation of the target function differs when different training data is used is referred to as variance. The relationship between the input (properties) and output variables is established by the target function (predicted temperature). The algorithm's tendency to continuously learn the erroneous thing by not taking all of the data into account is known as bias. Bias must be low for the model to be accurate.