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Training a model with scikit-learn

Completing the sklearn training pipeline

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Hey there! This problem might be a bit unpredictable, but give it a go and let us know how you do!

Consider the following code.


from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
#1. Load the data
iris = datasets.load_iris()
#2. Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=42)

#3. Apply StandardScaler normalization
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

#4. Initialize the model
clf = svm.SVC(kernel='linear', C=1)
#5. ---
#6. ---
#7. ---

What is the correct sequence of steps to complete the training and testing of the model?

Select one option from the list

5. Predict on the model using X_test, 6. Train the model using X_train, y_train, 7. Score the model using y_test

5. Score the model using y_test, 6. Train the model using X_train, y_train, 7. Predict on the model using X_test

5. Train the model using X_train, y_train, 6. Predict on the model using X_test, 7. Score the model using y_test and the predicted values

5. Train the model using X_train, y_train, 6. Score the model using y_test, 7. Predict on the model using X_test

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