When you’re building a machine learning model you’re faced with the bias-variance tradeoff, where you have to find the balance between having a model that: Is very expressive and captures the real patterns in the data. Generates predictions that are not too far off from the actual values, A model that is very expressive has a low bias, but it can also be too complex. While a model that generates predictions that aren’t too far off from the true value has low variance. Overfitting When the model is too complex and tries to encode more patterns from the training data than it’s actually…
This story continues at The Next Web