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Classification with Python
course content

Course Content

Classification with Python

Classification with Python

1. k-NN Classifier
What is ClassificationWhat is k-NNk-NN with Multiple FeaturesImplementing k-NNMulti-Class ClassificationTrain-test Split and Cross ValidationChallenge: Choosing the Best K Value
2. Logistic Regression
What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting and RegularizationChallenge: Classifying Inseparable Data
3. Decision Tree
What is Decision TreeSplitting the NodesPreventing Overfitting in Decision TreesChallenge: Implementing a Decision Tree
4. Random Forest
What is Random ForestThe Randomness of ForestChallenge: Implementing a Random Forest
5. Comparing Models
Confusion MatrixMetricsChallenge: Comparing ModelsSummary

book
Challenge: Comparing Models

Now you'll compare the models we've covered using a single dataset — the breast cancer dataset. The target variable is the 'diagnosis' column, where 1 represents malignant and 0 represents benign cases.

You will apply GridSearchCV to each model to find the best parameters. In this task, you'll use recall as the scoring metric because minimizing false negatives is crucial. To have GridSearchCV select the best parameters based on recall, set scoring='recall'.

Task

Swipe to start coding

The task is to build all the models we learned and to print the best parameters along with the best recall score of each model. You will need to fill in the parameter names in the param_grid dictionaries.

  1. For the k-NN model find the best n_neighbors value out of [3, 5, 7, 12].
  2. For the Logistic Regression run through [0.1, 1, 10] values of C.
  3. For a Decision Tree, we want to configure two parameters, max_depth and min_samples_leaf. Run through values [2, 4, 6, 10] for max_depth and [1, 2, 4, 7] for min_samples_leaf.
  4. For a Random Forest, find the best max_depth(maximum depth of each Tree) value out of [2, 4, 6] and the best number of trees(n_estimators). Try values [20, 50, 100] for the number of trees.

Solution

Note

The code takes some time to run(less than a minute).

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Section 5. Chapter 3
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book
Challenge: Comparing Models

Now you'll compare the models we've covered using a single dataset — the breast cancer dataset. The target variable is the 'diagnosis' column, where 1 represents malignant and 0 represents benign cases.

You will apply GridSearchCV to each model to find the best parameters. In this task, you'll use recall as the scoring metric because minimizing false negatives is crucial. To have GridSearchCV select the best parameters based on recall, set scoring='recall'.

Task

Swipe to start coding

The task is to build all the models we learned and to print the best parameters along with the best recall score of each model. You will need to fill in the parameter names in the param_grid dictionaries.

  1. For the k-NN model find the best n_neighbors value out of [3, 5, 7, 12].
  2. For the Logistic Regression run through [0.1, 1, 10] values of C.
  3. For a Decision Tree, we want to configure two parameters, max_depth and min_samples_leaf. Run through values [2, 4, 6, 10] for max_depth and [1, 2, 4, 7] for min_samples_leaf.
  4. For a Random Forest, find the best max_depth(maximum depth of each Tree) value out of [2, 4, 6] and the best number of trees(n_estimators). Try values [20, 50, 100] for the number of trees.

Solution

Note

The code takes some time to run(less than a minute).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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