<font color="#1E90FF"><strong>High bias</strong></font> represents that <strong>the model cannot capture the underlying the pattern of the data (i.e. underfitting)</strong>.<br>
Since the model is underfitting to even the training data, the difference between the score for the training data and that of test data is small.<br>
In case of high bias, <strong>increasing the number of features and changing the algorithm would help</strong> to improve the performance.<br>
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<font color="#1E90FF"><strong>Hibh variance</strong></font> represents that <strong>tha model is overfitting</strong> to the traning data.<br>
Therefore the difference between the score for the training data and that of test data is large.<br>
In case of high variance, <strong>getting more data and feature selection would help</strong> to improve the performance.