Binary classification and key concepts of it

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* 1. Choose applications that rely on classification.

Fraud detection.

All of the above.

Image classification.

Detecting sentence sentiment.

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* 2. In binary classification, what is the number of classes observed?

Not possible to say.

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* 3. How do we represent class labels to train an algorithm? (Suppose that we have two classes, cat and dog)

Map class names to integers.

Map class names to floats.

Just pass class names to the algorithm.

None of the variants.

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* 4. What does a logistic regression model estimate?

The name of the class.

None of the variants.

Probabilities of belonging to one of N classes.

The number of classes to output.

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* 5. What metric to use when we are dealing with imbalanced classes?

f1 score.

precision.

recall.

accuracy.

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* 6. What is the idea of using cross-validation?

To train and test classifier on different subsets of data and then receive the predefined score for each subset to understand model performance better.

To test model on all the data.

None of the variants.

To divide model into sub models and test them independently.

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* 7. How should we deal with NaN values in the dataset?

All the variants.

Drop all rows with NaN values.

Use KNN to impute missing values.

Fill NaN values with some unique token or statistics based measure.

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* 8. What was a goal of function choose_best_classifier?

None of the variants.

The function trains one classifier multiple times and denotes that it’s the best one.

The function applies greedy search to find the best model based on predefined metric.

The function randomly selects a classifier and denotes that it’s the best one.