The IIT Madras BS Machine Learning Practice (MLP) Quiz 2 paper sat on 6 Aug 2023, in the May 2023 term: 23 questions for 50 marks in 120 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.
Consider the following code and its output:
Code:
from sklearn.datasets import load_irisfrom sklearn.linear_model import LogisticRegression
X, y = load_iris(return_X_y=True)clf = LogisticRegression(random_state=0).fit(X, y)
print(y[70:80])print(clf.predict(X[70:80, :]))Output:
[1 1 1 1 1 1 1 1 1 1][2 1 1 1 1 1 1 2 1 1]What will be the output of the following code? Enter your answer correct to one decimal place.
print(clf.score(X[70:80, :], y[70:80]))Correct answer: 0.8
Correct answer: 1.00
What will be the output of the following code ?
from sklearn.neighbors import KNeighborsClassifierX_train = [[1,100],[4,400],[5,500],[6,600],[8,800],[9,900], [11,1100],[12,1200],[15,1500], [18,1800],[19,1900]]y_train = [0,0,1,1,1,2,2,2,2,2,2]
X_test = [[2,200]]
knn = KNeighborsClassifier(n_neighbors= len(y_train), metric="euclidean", weights= 'uniform')knn.fit(X_train,y_train)
print(knn.predict(X_test))Correct answer: 2
What will be the output of the following code?
import numpy as npfrom sklearn.impute import KNNImputerX = np.array([[5,6,3],[np.nan,1,5],[0,2,8],[4,4,2]])knn = KNNImputer(n_neighbors=2,weights="uniform")X_trf= knn.fit_transform(X)print(X_trf[1][0])Correct answer: 2
Correct answer
Your task to design a model that can predict label of an article, in order to help an online news website. The labels could be “political”, “sports” and “international”.
Following is the label matrix for random 3 articles:
What type of classification problem is this?
Binary class, single label classification.
Binary class, multi label classification.
Multi class, multi label classification.
Multi class, single label classification.
Correct answer
Multi class, multi label classification.
How does strong correlation between features given the labels impact the classification performance in Naive Bayes?
It has no impact because Naive Bayes assumes feature independence.
It improves the classification performance.
It degrades the classification performance.
It depends on the type of Naive Bayes variant used.
Correct answer
It degrades the classification performance.
When might the Precision-Recall curve be more informative than the ROC curve?
When the dataset is imbalanced.
When the dataset has equal numbers of positive and negative instances.
When the classifier has high accuracy.
When the classifier produces balanced precision and recall values.
Correct answer
When the dataset is imbalanced.
Correct answer
Which of the following parameters learned by the KNN(KNeighborRegressor) model while training?
Correct answer
> 1
< 1
=1
Cannot be determined
Correct answer
=1
Consider below code which of the following option is true for that
from sklearn.neighbors import NearestNeighborsneigh = NearestNeighbors(n_neighbors=4)neigh.fit(X_train)print(neigh.kneighbors(X_test[0:1]))Assume X_train and X_test are of type numpy.ndarray.
Correct answer
Which of these may NOT help in handling overfitting in decision trees?
Correct answer
Any predictor
only a decision tree predictor
only a linear model predictor
only a support vector predictor
Correct answer
Any predictor
Correct answer
There are 10,000 images, each contains either a cat or a dog. Exactly 500 contain cats and others contain dogs. Your task is to train a binary classifier.
Train a binary classifier to detect if an MRI image contains carcinogenic cells or not. Number of true positives are 2%.
Predicting if a chest x-ray belongs to a male patient or a female patient. There are nearly equal number of samples of each category.
Based on a student’s senior secondary marks and other features, predicting if he will fail a particular exam. The exam clearing rate is 98.23%.
Correct answers
There are 10,000 images, each contains either a cat or a dog. Exactly 500 contain cats and others contain dogs. Your task is to train a binary classifier.
Train a binary classifier to detect if an MRI image contains carcinogenic cells or not. Number of true positives are 2%.
Based on a student’s senior secondary marks and other features, predicting if he will fail a particular exam. The exam clearing rate is 98.23%.
Which of the following is correct?
Correct answers
Which of the following option is true?
Correct answer
0.0001
1
1000
Cannot be determined
Correct answer
1000
What will be the output of the following code:
from sklearn.feature_extraction.text import HashingVectorizercorpus = ['You can have it all. Just not all at once.', 'Train your mind to see the good in every situation.', 'What we think, we become.', 'If I got rid of my demons, I’d lose my angels.',]vectorizer = HashingVectorizer(n_features= 12,lowercase=True)X = vectorizer.fit_transform(corpus)print(X.shape[1])Correct answer: 12
Consider the following code. How many different parameter combinations will be tried in GridSearchCV?
from sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import GridSearchCVfrom sklearn.datasets import load_iris
X, y = load_iris(as_frame = True, return_X_y = True)
param_grid = [{'max_depth':range(1, 10, 2), 'min_samples_split': range(2, 10, 3)}, {'min_samples_leaf': range(1, 11, 3)}]gs = GridSearchCV(DecisionTreeClassifier(), param_grid, cv = 5)gs.fit(X,y)12
80
60
19
Correct answer
19
Which of the following is the most expected output for the code given below:
from sklearn.datasets import load_winefrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitX,y = load_wine(as_frame = True, return_X_y = True)
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.10, random_state = 12)
clf1 = DecisionTreeClassifier(ccp_alpha = 0.1, random_state = 81)
clf2 = DecisionTreeClassifier(ccp_alpha = 0.25, random_state = 81)
clf1.fit(X_train, y_train)clf2.fit(X_train, y_train)
print(clf1.score(X_train, y_train))print(clf2.score(X_train, y_train))print(clf1.get_depth())print(clf2.get_depth())0.9875
0.9125
2
3
0.9125
0.9875
2
3
0.9875
0.9125
3
2
0.9125
0.9875
3
2
Correct answer
0.9875
0.9125
3
2
Consider the following block of code:
from sklearn.datasets import load_winefrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitX,y = load_breast_cancer(as_frame = True, return_X_y = True)X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state = 1)clf = DecisionTreeClassifier(min_samples_split = 8, min_samples_leaf = 5, random_state = 5)clf.fit(X_train, y_train)print(clf.score(X_test, y_test))In which of the following scenarios, the split will be done at a node N?
Number of samples at node N = 5. If it is split, it will result in 3 samples in the left child and 2 samples in the right child.
Number of samples at node N = 10. If it is split, it will result in 5 samples in the left child and 5 samples in the right child.
Number of samples at node N = 15. If it is split, it will result in 9 samples in the left child and 6 samples in the right child.
Number of samples at node N = 8. If it is split, it will result in 5 samples in the left child and 3 samples in the right child.
Correct answers
Number of samples at node N = 10. If it is split, it will result in 5 samples in the left child and 5 samples in the right child.
Number of samples at node N = 15. If it is split, it will result in 9 samples in the left child and 6 samples in the right child.