docs: Add a sample to demonstrate the evaluation results

[DevStephanie]

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Fixes #<issue_number_goes_here> 🦕

[snippet-bot]

Here is the summary of changes.

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• samples/snippets/bqml_getting_started_test.py:95, tag bigquery_dataframes_bqml_getting_started_tutorial_evaluate
• samples/snippets/bqml_getting_started_test.py:166, tag bigquery_dataframes_bqml_getting_started_tutorial_predict

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[tswast]

Let's also mention that the results are in the same form a ML.EVALUATE here. This from the SQL description would be really important to include:

Because you performed a logistic regression, the results include the following columns:

precision — A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class.
recall — A metric for classification models that answers the following question: Out of all the possible positive labels, how many did the model correctly identify?
accuracy — Accuracy is the fraction of predictions that a classification model got right.
f1_score — A measure of the accuracy of the model. The f1 score is the harmonic average of the precision and recall. An f1 score's best value is 1. The worst value is 0.
log_loss — The loss function used in a logistic regression. This is the measure of how far the model's predictions are from the correct labels.
roc_auc — The area under the ROC curve. This is the probability that a classifier is more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive. For more information, see Classification in the Machine Learning Crash Course.

https://cloud.google.com/bigquery/docs/create-machine-learning-model#evaluate_your_model

[DevStephanie]

Yes, I agree. Will have those edits today.

[tswast]

Include the link to this course.

[DevStephanie]

Yes, will do!

[tswast]

The formatting is a bit funky / hard to read. Please add some bullet points and indentation:

Suggested change

	# precision — A metric for classification models. Precision identifies the frequency with
	# which a model was correct when predicting the positive class.
	# recall — A metric for classification models that answers the following question:
	# Out of all the possible positive labels, how many did the model correctly identify?
	# accuracy — Accuracy is the fraction of predictions that a classification model got right.
	# f1_score — A measure of the accuracy of the model. The f1 score is the harmonic average of
	# the precision and recall. An f1 score's best value is 1. The worst value is 0.
	# log_loss — The loss function used in a logistic regression. This is the measure of how far the
	# model's predictions are from the correct labels.
	# roc_auc — The area under the ROC curve. This is the probability that a classifier is more confident that
	# a randomly chosen positive example
	# is actually positive than that a randomly chosen negative example is positive. For more information,
	# see Classification in the Machine Learning Crash Course.
	# * precision -- A metric for classification models. Precision identifies the frequency
	# with which a model was correct when predicting the positive class.
	# * recall -- A metric for classification models that answers the following question:
	# Out of all the possible positive labels, how many did the model correctly identify?
	# * accuracy -- Accuracy is the fraction of predictions that a classification model got right.
	# * f1_score -- A measure of the accuracy of the model. The f1 score is the harmonic average of
	# the precision and recall. An f1 score's best value is 1. The worst value is 0.
	# * log_loss -- The loss function used in a logistic regression. This is the measure of how far the
	# model's predictions are from the correct labels.
	# * roc_auc -- The area under the ROC curve. This is the probability that a classifier is more
	# confident that a randomly chosen positive example is actually positive than that a randomly
	# chosen negative example is positive. For more information, see "Classification" in the
	# Machine Learning Crash Course at
	# https://developers.google.com/machine-learning/crash-course/classification/video-lecture

[DevStephanie]

Yes, that does make sense. I will do that now.

[tswast]

Thanks!

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