Classification in Depth with Scikit-Learn

# Introduction to Classification Algorithms

Throughout this project, our goal is to introduce the concept of classification and one of its simplest techniques, decision trees. We will also cover evaluation metrics and decision boundaries. Our focus will be on understanding how this technique can be used to solve real-world problems, such as predicting customer churn or detecting fraudulent activities.
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## Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

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### Predictions

Based on previous results, choice the correct answers.

input

### Accuracy score

Use the following code to compute the accuracy_score:

``````from sklearn.metrics import accuracy_score

y_pred = tree.predict(X)
accuracy_score(y_pred,y)
``````

Round to two significant decimal

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### Confusion Matrix

Select the correct answers using the information from the confusion matrix.

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### Create the response variable based on the columns Trump and Clinton

Separate the target and the features into two variables and create the response variable based on the columns Trump and Clinton.

Select the correct code to complete this task

There could be more than just one correct answer.

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### Decision Tree classifier

Initialize a Decision Tree classifier (name the model clf) and fit on the data with a random_state: 42 and max_depth : 3 (the maximum depth of our decision tree using the max_depth parameter).

Calculate the accuracy score of the training dataset. Select the correct answer.

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### Classification

Which of the following statements are True about classification?

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### Example of classification

Which of the following statements are examples of classification?

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### Training and testing phase

In which phase are model parameters adjusted?

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#### Verónica Barraza

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## Classification in Depth with Scikit-Learn

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