Trade-off bias variance is another very important concept in Statistics/Machine Learning.

The data points in blue como from $y(x)=\sin(x)+\epsilon$, where $\epsilon$ has normal distribution. The red curves are estimated using different samples. The figure "Large Variance and Small Bias" presents the original model, which is Radial basis function network with 24 gaussian bases.

The figure "Small Variance and Large Bias" presents the same model regularized.

Note that in the figure "Small Variance and Large Bias" the red curves are very close to each other (small variance). The same does not happen in the figure "Large Variance and Small Bias" (large variance).

Small Variance and Large Bias

Large Variance and Small Bias

From my computer methods and machine learning course.

Trade-off bias variance is another very important concept in Statistics/Machine Learning.

The data points in blue come from $y(x)=\sin(x)+\epsilon$, where $\epsilon$ has a normal distribution. The red curves are estimated using different samples. The figure "Large Variance and Small Bias" presents the original model, which is Radial basis function network with 24 gaussian bases.

The figure "Small Variance and Large Bias" presents the same model regularized.

Note that in the figure "Small Variance and Large Bias" the red curves are very close to each other (small variance). The same does not happen in the figure "Large Variance and Small Bias" (large variance).

Small Variance and Large Bias

Large Variance and Small Bias

From my computer methods and machine learning course.

Trade-off bias variance is another very important concept in Statistics/Machine Learning. The curves in Red use different samples:

Small Variance and Large Bias

Large Variance and Small Bias

From my computer methods and machine learning course.

Trade-off bias variance is another very important concept in Statistics/Machine Learning. 

The data points in blue como from $y(x)=\sin(x)+\epsilon$, where $\epsilon$ has normal distribution. The red curves are estimated using different samples. The figure "Large Variance and Small Bias" presents the original model, which is Radial basis function network with 24 gaussian bases.

The figure "Small Variance and Large Bias" presents the same model regularized.

Note that in the figure "Small Variance and Large Bias" the red curves are very close to each other (small variance). The same does not happen in the figure "Large Variance and Small Bias" (large variance).

Small Variance and Large Bias

Large Variance and Small Bias

From my computer methods and machine learning course.