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Ensemble Machine Learning by Ankit Dixit

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Bias and variance errors

Bias error: As you can see in the preceding equation, bias is the average difference between predicted and actual values; if our system shows high bias error, that means we are getting a low-performing or under fitting model:

Figure 1. 2: Bias error

The preceding figure shows a representation of bias error. A linear model is trained over complex data and you can clearly see the error between the prediction (red line) and actual output (black dots). This shows that our model is not fitting properly on the dataset and thus underperforming. We can avoid such cases by using a complex (polynomial) model rather than a simple ...

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