This is my 99th day of #100daysofcode and #python learning journey. Approximately I am in terminal point. Now I feel I am champion. Talking about today's progress I keep learning from DataCamp. I also did some exercises there. Did some codes on the random topic.
Centering and Scaling In a Pipeline
# Import the necessary modules from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline # Setup the pipeline steps: steps steps = [('scaler', StandardScaler()), ('knn', KNeighborsClassifier())] # Create the pipeline: pipeline pipeline = Pipeline(steps) # Create train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Fit the pipeline to the training set: knn_scaled knn_scaled = pipeline.fit(X_train, y_train) # Instantiate and fit a k-NN classifier to the unscaled data knn_unscaled = KNeighborsClassifier().fit(X_train, y_train) # Compute and print metrics print('Accuracy with Scaling: {}'.format(knn_scaled.score(X_test, y_test))) print('Accuracy without Scaling: {}'.format(knn_unscaled.score(X_test, y_test))) The output of above code is,
Accuracy with Scaling: 0.7700680272108843 Accuracy without Scaling: 0.6979591836734694
Day 99 Of #100daysofcode and #python
— Durga Pokharel (@durgacodes) April 7, 2021
Centering and scaling in a pipeline from DataCamp
One more day to reach in a terminal point. Finally I am going to complete 100daysofcode.#100DaysOfCode #WomenWhoCode #DEVCommunity pic.twitter.com/crjxj5Fzuy
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