Top Multiple Choice Questions (MCQ) for Machine Learning

Introduction to Machine Learning

Machine learning is the science of getting computers to learn without being explicitly programmed. It operates on the concept of understanding through experiences, allowing computers to learn autonomously without human interaction.

Scope of Machine Learning

The scope of machine learning is vast and covers several areas, including education, search engines, digital marketing, healthcare, spam protection, traffic alerts, social media, and Google Translate.

Limitations of Machine Learning

While machine learning offers many benefits, some limitations exist, including its dependence on training and learning, limited performance, the need for heterogeneous data sets for meaningful insights, and substantial resource requirements to learn various topics.

Types of Machine Learning

There are two types of machine learning: supervised learning and unsupervised learning.

Supervised learning involves using labeled data to predict outputs for new inputs, while unsupervised learning deals with unlabeled data and requires the machine to discover necessary information on its own.

Linear Regression

Linear regression is a popular machine learning algorithm that operates on supervised learning. It is used for predictive analysis and is the easiest algorithm to implement. Linear regression makes predictions for continuous variables such as product price, housing cost, or salary. The algorithm operates by targeting prediction values based on independent variables.

Artificial Neural Network (ANN)

An artificial neural network is a computation nonlinear model that is inspired by the brain. It uses a large collection of artificial neurons as processing elements to operate in parallel to classify information, make predictions, or perform other tasks.

ANNs are capable of learning by altering values and thus are often used in machine learning applications.

Machine Learning MCQs

There are several multiple-choice questions that can help assess an individual's knowledge of machine learning, including questions about supervised and unsupervised learning, artificial neural networks, and linear regression.

Identifying types of machine learning

Out of the given options, the type of machine learning that is not valid is Semi-supervised learning.

The other valid types of machine learning are:

  • Supervised Learning
  • Reinforcement Learning
  • Unsupervised Learning

Therefore, the correct answer is A) Semi-supervised learning.

// Example usage in a program: 
// Set type of machine learning
typeOfML = "supervised";

Identifying Learning Algorithms for Facial Identities and Facial Expressions

For facial identities and facial expressions, the learning algorithm that is used is "Recognition Patterns". This algorithm helps in identifying patterns in the facial features and expressions of individuals, and uses this information to recognize and differentiate between different faces and facial expressions. It is commonly used in facial recognition technology, surveillance systems, and other applications that require accurate and reliable pattern recognition capabilities.

Identifying a Model Trained with a Single Batch of Data

A model that is trained with data in a single batch is called as batch learning or offline learning.

Therefore, the answer is:

ANSWER - C) Both A and B

Where A stands for Offline Learning and B stands for Batch Learning.

Application of Machine Learning to Large Databases

In the field of computer science, the practice of using machine learning techniques to analyze and extract useful information from large databases is called data mining. It involves applying statistical and computational algorithms to large sets of data to identify patterns and relationships. This process is used to discover valuable insights and help businesses make informed decisions. Other applications of machine learning include artificial intelligence, big data computing, and the internet of things.

Identifying the Type of Learning with Labeled Training Data

In supervised learning, labeled training data is used for training a model. This type of learning is used when we have a set of input-output pairs and want the model to learn the mapping between inputs and outputs. The labeled data is used to teach the model to make predictions on new, unseen input data.

// example of supervised learning using Python's scikit-learn library
from sklearn import datasets
from sklearn import svm

# load the iris dataset
iris = datasets.load_iris()

# use the first two features as input data and the target variable as the output data
X =[:, :2]
y =

# create a support vector machine classifier and fit it to the training data
clf = svm.SVC(), y)

# make predictions on new, unseen data
new_data = [[5.0, 3.6], [6.2, 2.7]]
predicted_classes = clf.predict(new_data)


In this example, we use the iris dataset from scikit-learn and extract the first two features as input data and the target variable as the output data. We then create a support vector machine classifier and fit it to the training data. Finally, we use the model to make predictions on new, unseen data and print out the predicted classes.

Supervised learning is used in various applications such as image classification, natural language processing, and speech recognition.

Identifying the Number of Input Dimensions in PCA

It is true that in PCA (Principal Component Analysis), the number of input dimensions equals the number of principal components. This is because PCA works on reducing the number of dimensions in a dataset while maintaining the most important information. The number of input dimensions represents the number of features in the dataset that we are trying to reduce, and the number of principal components represents the number of new variables created after applying PCA.


If we have a dataset with 5 input dimensions, then the resulting PCA will also have 5 principal components.

Which factor does dimensionality reduction reduce?

The correct answer is D. Dimensionality reduction reduces collinearity.

Machine Learning Algorithm Based on Bagging

Random forest is a machine learning algorithm that is based on the idea of bagging.

Bagging stands for Bootstrap Aggregating, and it is a technique in machine learning that involves combining multiple models to produce a better overall model. In the case of random forest, multiple decision trees are combined to form a more robust and accurate classification or regression model.

Therefore, the correct answer is option B) Random forest.

 #python code example
from sklearn.ensemble import RandomForestClassifier #importing random forest classifier
model = RandomForestClassifier() #creating an instance of random forest classifier

Disadvantages of Decision Trees

Although decision trees have many benefits, they also have some disadvantages. One of the main drawbacks of decision trees is that they are prone to overfitting, which means they can create overly complex models that fit the training data too closely and do not generalize well to new data.

Understanding Machine Learning Terminology

In machine learning, the process of building a model based on sample data is termed as data training. The first step in this process is to select and prepare the training data. The machine learning algorithms use this training data to learn patterns and relationships in the data and create a model that can be used to make predictions or decisions.

Therefore, the correct answer to the given question is option B. The term known as on which the machine learning algorithms build a model based on sample data is called training data.

  # Example code for preparing and using Training Data in Machine Learning
  # Importing necessary libraries
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  # Load the dataset
  data = pd.read_csv("sample_data.csv")
  # Splitting the data into training and testing sets
  train_data, test_data, train_target, test_target = train_test_split(data.drop(columns=['target_variable']), 
  # Training the model on the training data
  model = LinearRegression(), train_target)
  # Predicting the outcome using the trained model on test data
  predictions = model.predict(test_data)

Machine Learning as a Subset of Artificial Intelligence

Machine learning is a specific area that falls under the broader category of artificial intelligence. It involves building algorithms that can learn from data, identify patterns, and make predictions or decisions. Deep learning is a more advanced form of machine learning that involves the use of neural networks to process large amounts of complex data. However, it is important to note that machine learning and deep learning are both subsets of AI, which encompasses a range of technologies and applications aimed at creating intelligent machines.

// No code provided in the question

Machine Learning Techniques for Outlier Detection

An important aspect of machine learning is to detect outliers in the data. This is critical because outliers can skew the results of the models and algorithms being used. There are several machine learning techniques available for detecting outliers, such as:

  • Classification
  • Clustering
  • Anomaly detection

Of these three techniques, the one that specifically identifies outliers is anomaly detection. Therefore, the correct answer to the question is C) Anomaly detection.

Who is considered the father of Machine Learning?

According to the given options, the father of machine learning is Geoffrey Everest Hinton.

// No code needed for this task

Significance of Crossover in Genetic Algorithm

In genetic algorithms, the crossover is considered the most significant phase. It is a process of combining genetic information from two parent individuals to create new offspring. This process is similar to biological reproduction, where two parents contribute their genetic material to their offspring.

Crossover helps to avoid stagnation and loss of genetic diversity, which is essential in generating the optimal solution. Using this process, new individuals can be generated with new genetic combinations that may lead to a better solution.

The other phases of the genetic algorithm, such as mutation, selection, and fitness function, also have critical roles, but crossover holds the most significant importance in the overall algorithm process.

Common Classes of Problems in Machine Learning

In machine learning, there are three commonly recognized classes of problems that algorithms are designed to solve:

  1. Regression: This involves predicting a continuous value, such as a price or a score.
  2. Classification: This involves predicting a discrete value, such as a label or a category.
  3. Clustering: This involves grouping data points together based on similarity without any prior knowledge of their labels.

All of these classes of problems are integral to the field of machine learning and are used in a wide variety of applications.

False Statements about Regression

Out of the given options, the false statement about regression is:

D) It discovers casual relationships

The other statements are true:

  • It is used for prediction
  • It is used for interpretation
  • It relates inputs to outputs

Note: Regression is a statistical method used to examine the relationship between two or more variables. It can be used to predict an outcome based on input variables and also to interpret the strength and direction of the relationship between the variables. However, it does not necessarily discover causal relationships as correlation does not imply causation.

Successful Applications of Machine Learning

Machine learning (ML) has found successful applications in various fields. Some of the most notable ones include:

- Learning to classify new astronomical structures
- Learning to recognize spoken words
- Learning to drive an autonomous vehicle

All of the above are examples of how ML is being used to achieve breakthroughs in science, technology, and innovation.

Identifying Incorrect Numerical Functions in Machine Learning

Out of the given numerical functions representing machine learning, one of them is incorrect. Let's check each one of them:

  • Support Vector Machines
  • Linear Regression
  • Neural Network
  • Case-based

After careful examination, it is revealed that the incorrect numerical function is Case-based.

  <!-- Sample code for identifying the incorrect numerical functions in machine learning -->
  numerical_functions = ['Support Vector Machines', 'Linear Regression', 'Neural Network', 'Case-based']

for function in numerical_functions:
    if function == 'Case-based':
        print("This is the incorrect numerical function in machine learning")


The FIND-S algorithm is a concept learning algorithm that finds the most specific hypothesis that fits all positive examples in the training data. It starts with the most specific hypothesis and generalizes it iteratively until it covers all the positive instances. In this process, it ignores the negative instances, as they do not contribute to building the hypothesis.

Therefore, the correct answer is option B) Negative.

// Implementation of FIND-S Algorithm
function findSAlgorithm(examples) {
  let hypothesis = examples[0];
  for (i = 1; i < examples.length; i++) {
    if (examples[i].isPositive()) {
      for (j = 0; j < hypothesis.length; j++) {
        if (hypothesis[j] != examples[i][j]) {
          hypothesis[j] = '?';
  return hypothesis;

Understanding Neuro Software

Neuro software refers to a type of software that is designed as a neural network, which allows it to simulate different types of neural networks. This powerful and easy-to-use software is used to analyze data and can be applied in various industries. It is highly efficient in handling large and complex datasets, and its application extends beyond the field of neuroscience. Therefore, option C is the correct definition of neuro software.

Confirming the Validity of Backpropagation Law

The statement that the backpropagation law is also known as the generalized Delta rule is true.

# Backpropagation algorithm # Also known as generalized delta rule

class NeuralNetwork:

def __init__(self, input_layer_size, hidden_layer_size, output_layer_size): pass

def train(self, X, y): pass

def predict(self, X): pass

Backpropagation Rule Limitations

The backpropagation algorithm is widely used in training neural networks. However, it has some limitations that need to be considered:

  • Slow convergence: The backpropagation algorithm can be slow in converging to a solution since it requires numerous iterations to update the weights and biases in the network.
  • Scaling: The backpropagation algorithm is sensitive to the scaling of the input data, which can cause convergence issues in certain scenarios.
  • Local minima problem: The backpropagation algorithm can get stuck in local minima during the optimization process, resulting in suboptimal solutions.
  • All of the above: All of the mentioned options are general limitations of the backpropagation rule.

It is important to note that while these limitations exist, there are various techniques and approaches that can be used to mitigate them.

Analysis of Machine Learning Algorithm Requirements

Machine learning algorithms require analysis using both statistical learning theory and computational learning theory.

// Example code using both statistical and computational learning theory
import numpy as np
from sklearn.linear_model import LinearRegression

# Load data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Reshape data
x = x.reshape((-1, 1))

# Create model
model = LinearRegression()

# Fit model, y)

# Predict response
y_pred = model.predict(x)

# Display results
print('Coefficients:', model.coef_)
print('Intercept:', model.intercept_)
print('Predicted response:', y_pred)

Choosing Model Evaluation Tools

When evaluating classification models, there are various tools that help developers to analyze their performance on different metrics. Some of the most widely used evaluation tools are:

- Area under the ROC curve - Confusion matrix - Cost-sensitive accuracy

All of these tools are important and should be used for a comprehensive analysis of model performance. Therefore, option D, "All of the above," is the correct answer.

Understanding PAC

In machine learning, PAC stands for Probably Approximate Correct. It refers to a theoretical framework for measuring the effectiveness of algorithms used in supervised learning. The goal of supervised learning is to create a model that can accurately predict outcomes for future data based on a labeled set of training data.

The PAC framework determines the number of training samples required for a learning algorithm to produce a model that is "approximately correct" with a certain level of confidence. The term "probably" indicates that the level of confidence is not 100%, but rather some probability less than that.

Therefore, PAC refers to the ability of a learning algorithm to generate a model that is "probably" correct for future predictions based on a finite training sample. This framework is widely used in the analysis of machine learning algorithms for both statistical and computational purposes.

Understanding True Error over Instance Space


The statement is true. True error is a measure of the error rate over the entire instance space and not just over the training data. It gives an idea about how well the model will perform on the unseen data. Unlike training error, true error takes into account the error that can be encountered on the unseen data. Hence, true error is a more realistic measure of model performance.

Components of CLT

The Central Limit Theorem (CLT) is comprised of:

- Mistake bound
- Sample complexity
- Computational complexity

Therefore, the correct option is D) All of the above.

Choosing an Instance-Based Learner

When it comes to instance-based learning, there are two approaches: eager and lazy learners. Out of these options, the correct choice for an instance-based learner is the lazy learner.

The lazy learner utilizes a similarity measure to classify new data points based on their similarity to already existing data points in the training set. This approach is known for being more flexible and for having a shorter training time than eager learners.

In contrast, eager learners try to construct a general model of the data during the training phase. This model can then be used to classify new data points without the need for the original training data. Eager learning is more resource-intensive and has a longer training time than lazy learning.

Difficulties with the k-Nearest Neighbor Algorithm

The k-Nearest Neighbor (k-NN) algorithm is a simple but effective algorithm used in classification problems. However, it has its own set of limitations and difficulties. Two of the main difficulties with the k-NN algorithm are:

  • Curse of dimensionality: As the number of features or dimensions increase, the distance between the data points becomes less meaningful. In high-dimensional spaces, the k-NN algorithm becomes inefficient and ineffective. This is known as the curse of dimensionality.
  • Large computation time: In order to classify a new test case, the k-NN algorithm needs to calculate the distance between the test case and all the training cases. This can be a very time-consuming task, especially when working with large datasets with many dimensions.

Therefore, option C) "Both A and B" is the correct answer.

//Example of K-NN algorithm in Python
from sklearn.neighbors import KNeighborsClassifier

#Create the K-NN classifier object
knn = KNeighborsClassifier(n_neighbors=3)

#Train the model using the training dataset, y_train)

#Predict the class labels of the test dataset
y_pred = knn.predict(X_test)

Number of Layers in Radial Basis Function Neural Networks

In radial basis function neural networks, there are three types of layers in total:

  1. Input layer: This layer receives the input data and passes it on to the next layer.
  2. Hidden layer: This layer performs calculations using radial basis functions to transform the data into a form that is easier for the output layer to process.
  3. Output layer: This layer produces the final output of the network based on the input data and the calculations performed by the hidden layer.

Therefore, the correct answer is 3.

Application of CBR

One of the applications of CBR is Design.

CBR stands for Case-Based Reasoning. It is an artificial intelligence approach where new problems are solved based on their similarity to previously solved problems. CBR is widely used in various fields like medicine, engineering, and planning.

Out of the given options, the correct answer is B) Design.

Advantages of Case-Based Reasoning (CBR)

CBR has several advantages, including:

  • Fast training
  • A local approximation is found for each test case
  • The knowledge is in a form that is understandable by humans

Therefore, option D is correct that all of the above advantages apply to CBR.

Note: No code provided for this task as it is a theoretical question.

Types of Search and Optimization Algorithms in Machine Learning

In the field of machine learning, there are various search and optimization algorithms used, out of which one is not considered as evolutionary computation. Let's identify the algorithm that is not evolutionary computing from the following list:

  • Genetic algorithm
  • Genetic programming
  • Neuroevolution
  • Perceptron

Correct Answer: Perceptron

Explanation: Perceptron is an algorithm for supervised learning of binary classifiers, whereas the other three algorithms mentioned above, i.e., genetic algorithm, genetic programming, and neuroevolution, are evolutionary computation techniques used for optimization problems like those in genetic programming and for neural network training in neuroevolution.Artificial Intelligence

Understanding AI

Artificial Intelligence (AI) is the process of enabling machines or computers to perform tasks that typically require human intelligence to accomplish. This involves creating algorithms and models that can process large amounts of data and make decisions based on that data, similar to how humans make decisions. AI systems are designed to learn and adapt, which allows them to improve their performance over time.

One common misconception about AI is that it is designed to completely replicate human intelligence. While AI can perform tasks that were once limited to humans, it is not capable of truly understanding the world in the same way that humans do. However, AI has the potential to transform industries and revolutionize the way we live and work.

Identifying Non-Machine Learning Disciplines

Option D, Neuro statistics, is not a discipline in machine learning. The other options, Information Theory, Optimization + Control, and Physics, are all related to machine learning.

# Python code to identify non-machine learning disciplines

non_ml_discipline = "Neuro statistics"

ml_disciplines = ["Information Theory", "Optimization + Control", "Physics"]

if non_ml_discipline in ml_disciplines: print("The discipline is related to machine learning.") else: print("The discipline is not related to machine learning.")


The K mean algorithm is an iterative algorithm used for clustering in Machine Learning. The value of K determines the number of clusters to be formed. K is not related to the number of data or attributes but it stands for the predefined number of iterations in the algorithm. It defines the number of times the clustering process is executed until the optimal cluster centroids are obtained.

Option D: The correct answer is D) Number of iterations.

Can Decision Trees be Used for Clustering?

False. Decision trees cannot be used for clustering. They are a type of supervised learning method that works well for classification and regression problems. On the other hand, clustering is an unsupervised learning technique used to group similar data points together based on certain similarities or characteristics.

Clustering Method that Accounts for Variance in Data

The clustering method that takes care of variance in data is the Gaussian mixture model, which is represented by option B.

This model is used to determine the distribution of clusters in a dataset by identifying the probability density function of each class of data points. It is useful for identifying clusters with different variances and for handling complex datasets where each cluster may not be in a spherical shape.

On the other hand, decision tree and k-means clustering algorithms do not directly take into account the variance in the data.

Identifying Supervised Learning

In supervised learning, the algorithm model is trained on labeled data where the input features and their corresponding target values are already known. The algorithm then makes predictions on new data based on the patterns observed in the training phase. Here are the examples of some supervised learning algorithms:

  • Naive Bayes
  • Linear Regression
  • Decision Trees

However, Principal Component Analysis (PCA) is not considered as a supervised learning algorithm since it is an unsupervised approach used for dimensionality reduction and data visualization.


# Example of Naive Bayes Classifier in Python

from sklearn.naive_bayes import GaussianNB
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Load iris dataset
iris = datasets.load_iris()

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.3, random_state=1)

# Fit the model on the training data
gnb_model = GaussianNB(), y_train)

# Make predictions on test data
y_pred = gnb_model.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)

# Print the accuracy score
print("Accuracy:", accuracy)


Unsupervised learning is a type of machine learning where the algorithm is not given any labeled or classified data to train on. In unsupervised learning, the algorithm tries to find patterns or groupings in the data on its own, without any explicit feature or group information provided to it. Therefore, the number of groups is not known before training the algorithm, making this option the correct answer.


There is no code for this question as it is a concept-based question.

Identifying Machine Learning Algorithm

Out of the given options, the correct answer is B) SVG, which is not a machine learning algorithm. The other options, SVM and Random Forest, are both popular machine learning algorithms used for classification and regression tasks.

# Python code for identifying machine learning algorithm 

# Import necessary libraries
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier

# Instantiate the algorithms
model_svm = svm.SVC()
model_rf = RandomForestClassifier()

# Print the algorithms

Understanding Machine Learning

Machine Learning (ML) is a field of computer science that focuses on allowing computer systems to learn from experience without being explicitly programmed or requiring human intervention. ML extracts patterns from raw data using an algorithm or method, making it a type of artificial intelligence. Therefore, option D) All of the above is true about machine learning.

// No code provided in the question

Identification of Machine Learning

Out of the options given, rule-based inference is not considered as machine learning.

Answer: B) Rule-based inference is not machine learning.

Method Used for trainControl Resampling

The method used for trainControl resampling is repeatedcv. This method is used to repeatedly split data into training and testing sets, and is commonly used in machine learning algorithms like support vector machines (SVM). The other options listed ("svm" and "Bag32") are not methods used for trainControl resampling.

Identifying the Function to Create Common Graph Types

In order to create common graph types, the function "qplot" is typically used.


is part of the ggplot2 package in R programming language. It provides a quick method of generating plots with sensible defaults, such as creating scatter plots or density plots. The qplot function takes in data, x and y variables, and additional parameters to customize the plot.

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