Consider a regression dataset, the features are "Temperature" and "Humidity", and the label is "Precipitation" (i.e. rain fall in centimeter), both the features are numerical and there are no missing values in the dataset. Following code snippet trains a simple model on this dataset, assume necessary imports: data = pd.read_csv('dataset.csv') X = data[data.columns[:-1]] y = data[data.columns[-1]] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.8) mms = MinMaxScaler() X_train['Temperature'] = mms.fit_transform(X_train['Temperature']) X_train['Humidity'] = mms.fit_transform(X_train['Humidity']) X_test['Temperature'] = mms.fit_transform(X_test['Temperature']) X_test['Humidity'] = mms.fit_transform(X_test['Humidity']) lr = LinearRegression().fit(X_train,y_train) Choose the correct statements from the options:

(B) The training set size is smaller than test set size.

(C) The train and test samples are not scaled appropriately.

(D) One of the fundamental assumption of Machine Learning, which is, training and test data belong to same distribution, is not upheld.

(A) The training set will have 20% of the data, which is a good practice.

Machine Learning Practice · Quiz 1 · 16 Jul 2023 · May 2023 term

Question 3: Consider a regression dataset, the features are "Temperat…

Question 3

+2 marksOne or more correct options

Consider a regression dataset, the features are "Temperature" and "Humidity", and the label is "Precipitation" (i.e. rain fall in centimeter), both the features are numerical and there are no missing values in the dataset. Following code snippet trains a simple model on this dataset, assume necessary imports:

python

1data = pd.read_csv('dataset.csv')
2X = data[data.columns[:-1]]
3y = data[data.columns[-1]]
4X_train, X_test, y_train, y_test = train_test_split(X, y,
5                                                        test_size = 0.8)
6mms = MinMaxScaler()
7X_train['Temperature'] = mms.fit_transform(X_train['Temperature'])
8X_train['Humidity'] = mms.fit_transform(X_train['Humidity'])
9
10
11X_test['Temperature'] = mms.fit_transform(X_test['Temperature'])
12X_test['Humidity'] = mms.fit_transform(X_test['Humidity'])
13
14lr = LinearRegression().fit(X_train,y_train)

Choose the correct statements from the options:

Select all that apply.

A
The training set will have 20% of the data, which is a good practice.
B
The training set size is smaller than test set size.
C
The train and test samples are not scaled appropriately.
D
One of the fundamental assumption of Machine Learning, which is, training and test data belong to same distribution, is not upheld.

Show answer

Correct answers

B
The training set size is smaller than test set size.
C
The train and test samples are not scaled appropriately.
D
One of the fundamental assumption of Machine Learning, which is, training and test data belong to same distribution, is not upheld.

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Question 3 of 24 in the IIT Madras BS Machine Learning Practice (MLP) Quiz 1 paper sat on 16 Jul 2023, in the May 2023 term (IIT M DIPLOMA AN2 EXAM QPD2 16 JULY 2023). It carries 2 marks.

Question 3: Consider a regression dataset, the features are "Temperat…

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