Branch-AI/one_exercise_per_file/week02/day03/ex06/readme.md

Exercise 6 Pipeline

The goal of this exercise is to learn to use the Scikit-learn object: Pipeline. The data set: used for this exercise is the iris data set.

Preliminary:

• Run the code below.

  iris = load_iris()
  X, y = iris['data'], iris['target']
  
  #add missing values
  X[[1,20,50,100,135], 0] = np.nan
  X[[2,5,88,135], 1] = np.nan
  X[[4,15], 2] = np.nan
  X[[40,135], 3] = np.nan
  

• Split the data set in a train set and test set (33%), fit the Pipeline on the train set and predict on the test set. Use random_state=43.

The pipeline you will implement has to contain 3 steps:

• Imputer (median)
• Standard Scaler
• LogisticRegression

1. Train the pipeline on the train set and predict on the test set. Give the score of the model on the test set.