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SUP-3: Classificati...

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# SUP-3: Classification

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## Model Building

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### Evaluation

For regression problems, we are familiar with common metrics such as Root Means Square Error (RMSE) and the Coefficient of Determination (R2value).

With classification problems, we need a different set of metrics to evaluate the model. Here, we use metrics such as:

• Confusion Matrix
• Precision
• Recall
• F1 score
• ROC and AUC

Read the following blog posts to get familiar with these terms:

Once you are done, proceed to the next cells.

In :

```---------------------------------------------------------------------------
NotFittedError                            Traceback (most recent call last)
in
1 # Apply the model on the test data
----> 2pred_test = model.predict(x_test)

D:\Python\lib\site-packages\sklearn\utils\metaestimators.py in (*args, **kwargs)
114
115         # lambda, but not partial, allows help() to work with update_wrapper
--> 116out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
117         # update the docstring of the returned function
118         update_wrapper(out, self.fn)

D:\Python\lib\site-packages\sklearn\pipeline.py in predict(self, X, **predict_params)
417         Xt = X
418         for _, name, transform in self._iter(with_final=False):
--> 419Xt = transform.transform(Xt)
420         return self.steps[-1][-1].predict(Xt, **predict_params)
421

D:\Python\lib\site-packages\sklearn\compose\_column_transformer.py in transform(self, X)
557
558         """
--> 559check_is_fitted(self)
560         X = _check_X(X)
561         if hasattr(X, "columns"):

D:\Python\lib\site-packages\sklearn\utils\validation.py in check_is_fitted(estimator, attributes, msg, all_or_any)
965
966     if not attrs:
--> 967raise NotFittedError(msg % {'name': type(estimator).__name__})
968
969

NotFittedError: This ColumnTransformer instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.
```
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