Underfitting and Overfitting in Machine Learning ?
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Underfitting and Overfitting in Machine Learning:
Underfitting:
A machine learning algorithm is having underfitting when it can not capture the trend of the data.It's destroys the accuracy of our machine learning model. It usually happens when we have less data to build a model and also when we try to build a linear model with a non-linear data.
Overfitting:
A statistical model is said to be overfitted, when we train it with a lot of data. because of too much of details and noise.its a non linear methods in machine learning. A solution to remove overfitting is using a linear algorithm using decision trees.