Overfitting and Underfitting With Machine Learning Algorithms|avoid over...

The video is all about what is underfit and overfitting in
machine learning.

The tips are provided for how to avoid overfitting.

The accuracy need not to be 100% but the practically lesser
loss algorithm becomes famous one.

Underfit occurs when we choose a simple straight line as the
differential boundary between two classes.

Overfit occurs if the differential line accurately matches
the features of data set.

To overcome overfit we can just decrease the complexity of
boundary line.

Increasing the number of dataset doesn't help in both the
cases of underfit and overfit.

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Last video is on compare between common algorithm and
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