Okay, or Python we can evaluate our model to care order accuracy precision and recall using this our SK learn matrix. So, we can evaluate accuracy classification refine our confusion matrix. So confusion matrix will be more for this classification and then for those are a square or those residue or those are some r square there will be more for prediction and nodosa linear regressions So, we can evaluate our model using something like this. So, we need to train an accuracy okay then process that accuracy score So here's why test prediction okay I like him praying confusion matrix a confusion matrix K. So confusion matrix why test prediction? Can I can bring the classification report, show report why test and then the prediction Okay I can run the code there should be some error Okay, I need to convert those numbers or those protein point to a string.

So I can put some t ly str convert to string and I need to convert this one to string also Qaeda I can run the code again. Okay, so now I have accuracy one and then confusion matrix and another classification report. So we have a precision recall f1 score and support. Again it made this a nicer So, I can do something like this Okay accuracy one means around 100%. So, we need to evaluate chedda model in real world, real world projects. And then this is our confusion matrix.

And then he saw the classification report. So to actually create this confusion, metric accuracy and classification report. We also need to import from SK learn entries. Accuracy score classification require the confusion matrix