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WorksheetsQuiz on machine learning
Total questions: 20
Worksheet time: 10mins
In a classification problem, the outputs are
categorical or discrete
numerical or continuous
In a regression problem, the output is _
categorical or discrete
numerical or continuous
What is the difference between K-means and KNN Algorithms?
K-means is supervised while KNN is unsupervised
K-Means is used for clustering while KNN is used for classification and regression
KNeighborsClassifier class can be imported as:
from sklearn.ensemble import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import KNeighborsClassifier
from sklearn import KNeighborsClassifier
Which of the following is FALSE about SVM?
SVM has inbuilt L2 regularization capabilities
SVM solves both classification and regression problems
It does not require any feature scaling
Choosing an appropriate Kernel function is difficult
Which of the following is FALSE about Entropy in context of Decision Tree?
Entropy keeps on increasing as we keep splitting the nodes
Entropy is calculated using Information Gain
None of the above
Which of the following is FALSE about Random Forest and Adaboost?
Random Forest aims to decrease variance and not bias
Adaboost aims to decrease bias not variance
Both Adaboost and Random Forest aim to decrease both bias and variance
None of the above
Which of the following is TRUE about Random Forest and Adaboost?
Order of trees matters in Random Forest
Order of trees matters in Adaboost
Order of trees does not matter in Random Forest and Adaboost
None of the above
Which of the following is FALSE about Boosting?
Each weak learner has equal say in the final decision
Sequence in which each weak learner is created does not matter
None of the above
Which of the following is TRUE about Reinforcement Learning?
It uses rewards and penalty methods to train a model
It is also known as a semi - supervised learning model
Chess playing computer is a good example of reinforcement learning
All of the above
Which of the following algorithm is called Lazy Learner?
KNN
SVM
Naive Bayes
Decision Tree
Which of the following is NOT an example of hyper-parameter?
Number of nearest neighbors in KNN
Number of outliers in KNN
Depth of a Decision Tree
Number of trees in a Random Forest
Which of the following is FALSE about Tuples in Python?
Tuples are immutable
Tuple items can be deleted using del keyword
Tuples can be declared as: x,y=1,2
None of the above
Which of the following is TRUE about Random Forest?
Random Forest aims to decrease both variance and bias
Random Forest aims to decrease variance not bias
Random Forest aims to decrease bias not variance
None of the above
Dimensionality Reductions is useful because it:
reduces overfitting
reduces computation time
removes multicollinearity
All of the above
What do minimum missing value ratio represent?
The number of missing values in a dataset
The number of minimum missing values for an operation to be performed
The number of missing values in a column
The minimum ratio of missing values to all values in a column for cleaning to be performed
Which of these are not displayed when you see the import data result visualization?
Unique values
Missing values
Standard deviation
Mode
Compute target is the target variable whose value is to predicted.
True
False
'Convert to CSV' module is a part of which section?
Data Input and Output
Data Transformation
Model Training
None of the above
The key challenge to successfully train a machine learning model
Feature engineering and selection
Selection of the algorithm
Tuning of the hyperparameters
Evaluation of the metrics
All the above
