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Classification with Python
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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. Cross ValidationChallenge: Choosing the Best K Value.k-NN Summary
2. Logistic Regression
What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting. RegularizationChallenge: Classifying Unseparateble DataLogistic Regression Summary
3. Decision Tree
What is Decision TreeSplitting the NodesCrazy TreeChallenge: Implementing a Decision TreeDecision Tree Summary
4. Random Forest
What is Random ForestThe Randomness of ForestChallenge: Implementing a Random ForestRandom Forest Summary
5. Comparing Models
Confusion MatrixMetricsChallenge: Comparing ModelsSummary

bookChallenge: Comparing Models

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the 'diagnosis' column (1 – malignant, 0 – benign).

We will apply GridSearchCV to each model to find the best parameters. Also, in this task, we would use the recall metric for scoring since we do not want to have False Negatives. GridSearchCV can choose the parameters based on the recall metric if you set scoring='recall'.

Tarefa

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.

Note

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 5. Capítulo 3
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bookChallenge: Comparing Models

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the 'diagnosis' column (1 – malignant, 0 – benign).

We will apply GridSearchCV to each model to find the best parameters. Also, in this task, we would use the recall metric for scoring since we do not want to have False Negatives. GridSearchCV can choose the parameters based on the recall metric if you set scoring='recall'.

Tarefa

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.

Note

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 5. Capítulo 3
toggle bottom row

bookChallenge: Comparing Models

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the 'diagnosis' column (1 – malignant, 0 – benign).

We will apply GridSearchCV to each model to find the best parameters. Also, in this task, we would use the recall metric for scoring since we do not want to have False Negatives. GridSearchCV can choose the parameters based on the recall metric if you set scoring='recall'.

Tarefa

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.

Note

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the 'diagnosis' column (1 – malignant, 0 – benign).

We will apply GridSearchCV to each model to find the best parameters. Also, in this task, we would use the recall metric for scoring since we do not want to have False Negatives. GridSearchCV can choose the parameters based on the recall metric if you set scoring='recall'.

Tarefa

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.

Note

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 5. Capítulo 3
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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