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Machine Learning
Supervised Learning
Classification
Regression
Time Series Forecasting
Unsupervised Learning
Clustering
K-Means Clustering
Hierarchical Clustering
Semi-Supervised Learning
Reinforcement Learning(ML)
Deep Learning(ML)
Transfer Learning(ML)
Ensemble Learning
Explainable AI (XAI)
Bayesian Learning
Decision Trees
Support Vector Machines (SVMs)
Instance-Based Learning
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Neural Networks
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Meta-Learning
Multi-Task Learning
Metric Learning
Few-Shot Learning
Adversarial Learning
Data Pre Processing
Natural Language Processing(ML)
Machine Learning Quiz Questions
1.
What is the role of a validation set in supervised learning?
A. To train the model
B. To test the model's performance on new, unseen data
C. To tune the model's hyperparameters and select the best model
D. To preprocess the input data
view answer:
C. To tune the model's hyperparameters and select the best model
2.
What is underfitting in supervised learning?
A. A model that is too complex and has learned the noise in the training data
B. A model that performs well on new, unseen data
C. A model that is too simple and does not capture the underlying patterns in the data
D. A model that performs well on both training and testing data
view answer:
C. A model that is too simple and does not capture the underlying patterns in the data
Explanation:
Underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data. This causes the model to perform poorly on both the training and testing data. Among the given options, option C correctly describes underfitting in supervised learning.
3.
Which of the following is a common method for splitting data into training and testing sets?
A. k-means clustering
B. Principal Component Analysis (PCA)
C. k-fold cross-validation
D. Random sampling
view answer:
C. k-fold cross-validation
4.
Which of the following is an example of a supervised learning algorithm?
A. K-means clustering
B. Apriori
C. Decision tree
D. t-SNE
view answer:
C. Decision tree
5.
What is the purpose of using a loss function in supervised learning?
A. To identify input features
B. To measure the error between predicted and actual outputs
C. To optimize hyperparameters
D. To find the best model architecture
view answer:
B. To measure the error between predicted and actual outputs
6.
What is supervised learning?
A. A type of unsupervised learning
B. A type of deep learning
C. A type of machine learning where the model is trained on labeled data
D. A type of reinforcement learning
view answer:
C. A type of machine learning where the model is trained on labeled data
Explanation:
Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that the training data has both input features and corresponding output labels. The goal of supervised learning is to learn a function that maps the input to the output labels accurately, such that the function can be used to predict the output for new, unseen inputs. Among the given options, option C correctly describes supervised learning.
7.
Which of the following is a supervised learning task?
A. Clustering
B. Dimensionality reduction
C. Regression
D. Anomaly detection
view answer:
C. Regression
Explanation:
Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that the training data has both input features and corresponding output labels. The goal of supervised learning is to learn a function that maps the input to the output labels accurately, such that the function can be used to predict the output for new, unseen inputs. Among the given options, option C correctly describes a supervised learning task.
8.
What are the two main types of supervised learning problems?
A. Clustering and regression
B. Regression and classification
C. Dimensionality reduction and clustering
D. Anomaly detection and dimensionality reduction
view answer:
B. Regression and classification
Explanation:
Supervised learning problems can be broadly classified into two types: regression and classification. In regression problems, the goal is to predict a continuous output variable, such as the price of a house given its features. In classification problems, the goal is to predict a categorical output variable, such as whether an email is spam or not based on its content. Among the given options, only option B correctly describes the two main types of supervised learning problems.
9.
What is the main goal of supervised learning?
A. To learn the best possible mapping from inputs to outputs
B. To find hidden patterns in the data
C. To optimize the rewards in a given environment
D. To compress the data into a lower-dimensional space
view answer:
A. To learn the best possible mapping from inputs to outputs
Explanation:
The main goal of supervised learning is to learn a function that accurately maps the input to the output labels, such that it can be used to predict the output for new, unseen inputs. Among the given options, only option A correctly describes the main goal of supervised learning.
10.
In supervised learning, what is a label?
A. A variable used to split the data
B. A type of algorithm used for learning
C. The target or output variable associated with an instance
D. An error metric used to evaluate model performance
view answer:
C. The target or output variable associated with an instance
Explanation:
In supervised learning, a label is the target or output variable associated with an instance. The input to the model consists of features or attributes that describe the instance, and the label is the output that we want the model to predict given the input. Among the given options, option C correctly describes what a label is in supervised learning.
11.
What is overfitting in supervised learning?
A. A model that performs poorly on training data
B. A model that performs poorly on new, unseen data
C. A model that performs well on both training and testing data
D. A model that is too complex and has learned the noise in the training data
view answer:
B. A model that performs poorly on new, unseen data
Explanation:
Overfitting occurs when a model is too complex and has learned the noise in the training data, instead of the underlying pattern. This causes the model to perform well on the training data, but poorly on new, unseen data. Among the given options, option D correctly describes overfitting in supervised learning.
12.
Which of the following neural networks has a memory?
1D CNN
2D CNN
LSTM
None
view answer:
LSTM
13.
Which is the following is true about neurons?
A. A neuron has a single input and only single output
B. A neuron has multiple inputs and multiple outputs
C. A neuron has a single input and multiple outputs
D. All of the above
view answer:
D. All of the above
14.
Which of the following is an example of deep learning?
A. Self-driving cars
B. Pattern recognition
C. Natural language processing
D. All of the above
view answer:
D. All of the above
15.
Which of the following statement is not correct?
A. Neural networks mimic the human brain
B. It can only work for a single input and a single output
C. It can be used in image processing
D. None
view answer:
B. It can only work for a single input and a single output
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