Which of the following does not include Different learning methods

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01. What is Machine learning?

1. The autonomous acquisition of knowledge through the use of computer programs
2. The autonomous acquisition of knowledge through the use of manual programs
3. The selective acquisition of knowledge through the use of computer programs
4. The selective acquisition of knowledge through the use of manual programs

Answer : A
Explanation: “Machine learning” is the autonomous acquisition of knowledge through the use of computer programs.

02. What is true about Machine Learning?

1. Machine Learning (ML) is the field of computer science
2. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method
3. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention
4. All of the above

Answer : D
Explanation: All the statements are true about Machine Learning.

03. ML is a field of AI consisting of learning algorithms that?

1. Improve their performance
2. At executing some task
3. Over time with experience
4. All of the above

Answer : D
Explanation: Machine learning is a field of AI consisting of learning algorithms that: Improve their performance (P), At executing some task (T), Over time with experience (E).

04. Different learning methods do not include?

1. Memorization
2. Analogy
3. Introduction
4. Deduction

Answer : C
Explanation: Different learning methods in the ML do not include Introdution.

05. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

1. Decision Tree
2. Random Forest
3. Regression
4. Classification

Answer : B
Explanation: Random Forest

06. High entropy means that the partitions in classification are

1. pure
2. not pure
3. useful
4. useless

Answer : B
Explanation: Entropy is a measure of the randomness in the information being processed So the higher the entropy, the harder it is to draw any conclusions from that information. Entropy is a measure of disorder or purity or unpredictability or uncertainty. So Low entropy means less uncertain and high entropy means more uncertain.

07. Which of the following are ML methods?

1. Based on human supervision
2. Supervised Learning
3. Semi-reinforcement Learning
4. All of the above

Answer : A
Explanation: The following are various Machine learning methods based on some broad categories: Based on human supervision, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning.

08. In language understanding, the levels of knowledge do not include?

1. Phonological
2. Syntactic
3. Empirical
4. Logical

Answer : C
Explanation: In language understanding, the levels of knowledge do not include empirical knowledge.

09. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

Answer : D Explanation: Maximum possible different examples are the products of the possible values of each attribute and the number of classes so the result would be

3 * 2 * 2 * 2 * 3 = 72

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1. Drop missing rows or columns
2. Replace missing values with mean/median/mode
3. Assign a unique category to missing values
4. All of the above

Answer : D
Explanation: All of the above techniques are different ways of imputing the missing or corrupted data in a dataset.

1. Confusion matrix
2. Cost-sensitive accuracy
3. Area under the ROC curve
4. All of the above

Answer : D
Explanation: None

1. Language units
2. Structural units
3. Role structure of units
4. System constraints

Answer : B
Explanation: A model of language consists of categories which does not include structural units.

1. Decision Trees
2. Model-based clustering
3. K-means clustering
4. Density-based clustering

Answer : D
Explanation: The density-based clustering methods recognize clusters based on the density function distribution of the data object. For clusters with arbitrary shapes, these algorithms connect regions with sufficiently high densities into clusters.

1. Factor analysis
2. Decision trees are robust to outliers
3. Decision trees are prone to be overfit
4. None of the above

Answer : C
Explanation: Allowing a decision tree to split to a granular degree makes decision trees prone to learning every point extremely well to the point of perfect classification that is overfitting.

1. Assumes that all the features in a dataset are equally important
2. Assumes that all the features in a dataset are independent
3. Both A and B
4. None of the above options

Answer : C
Explanation: None

Answer : A
Explanation: p → Øq is not a horn clause from the above options.

1. Stop Word Removal
2. Stemming
3. Lemmatization
4. None of the above

Answer : A
Explanation: Stop word removal is not but Lemmatization and stemming are the techniques of keyword normalization.

1. Choose k to be the smallest value so that at least 99% of the varinace is retained
2. Use the elbow method
3. Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer)
4. Choose k to be the largest value so that 99% of the variance is retained

Answer : A
Explanation: Choose k to be the smallest value so that at least 99% of the variance is retained and This will maintain the structure of the data and also reduce its dimension.

1. Data points with outliers
2. Data points with different densities
3. Data points with nonconvex shapes

1. 1 & 2
2. 1, 2, & 3
3. 2 & 3
4. 1 & 3

Answer : B
Explanation: K-means clustering algorithm of Machine Learning fails to give good results when the data contains outliers, the density spread of data points across the data space is different, and when the data points with nonconvex shapes.