Regarding evaluating a model in Python, there are a number of different ways that you can go about doing this. Of course, the first thing that you need to do is to make sure that you have a good understanding of what your data looks like. This means that you should take some time to plot your data and get a feel for what it looks like. Once you have done this, you can start to look at some of the more technical aspects of your data.
One way to evaluate a model in Python is by looking at the coefficients of determination. This is a value between 0 and 1 that tells you how well your model fits your data. If your coefficient of determination is close to 1, then this means that your model fits your data well. However, if it is close to 0, then this means that your model does not fit your data very well.
Another way to evaluate a model in Python is by looking at the root mean squared error. This tells you how far off from the true values your predictions are on average. The lower the root mean squared error, the better job your model is doing at predicting values.
Finally, another way to evaluate a model in Python is by looking at the R-squ