If the machine learning model is not generalised then the model contains some kind of error.
Error= difference between actual and predicted values/classes
Formulae = sum of (actual output-predicted output), Also Error is the sum of reducible + irreducible error.
Reducible Error= bias + variance
Bias is how far is the predicted values/class from actual values/class. If the predicted value is too far away from actual value then the model is highly biased.
If values are not too far away then its low biased.
If the model is Highly biased then it won’t be able to capture the complex data and hence it UNDERFITS. (Underfitting)
If the model performs well on training dataset but does not perform well on testing or validation data which is new to model then its termed as the variance. So variance is how scattered predicted values from the actual values. If the model has High variance than the model overfits (OVERFITTING). Oftenly termed as the model learned the noise.
While solving our problems on python, Many of us might have faced the situation of kernels, the specific package supports only python 2.7 and require python 3 and there are a lot of issues while installing python kernels and running it with Jupyter notebook side by side. Here’s my solution of running python 2 and 3 on the same machine.
System Overview: I ran the kernels on MacOS Mojave version 10.14.5