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Unit 5: Dimensionality Reduction and Neural Networks - Practice Quiz

INT234 — Predictive Analytics 50 Questions
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1 What is the primary objective of dimensionality reduction in predictive analytics?

A. To create artificial data points for training
B. To increase the number of features to capture more data
C. To increase the computational complexity of the model
D. To reduce the number of input variables while retaining essential information

2 Which phenomenon refers to the problem where the amount of data needed to generalize accurately grows exponentially with the dimensionality?

A. The Law of Large Numbers
B. The Curse of Dimensionality
C. The Overfitting Paradox
D. The Vanishing Gradient

3 Principal Component Analysis (PCA) is best described as which type of machine learning technique?

A. Unsupervised Learning
B. Reinforcement Learning
C. Supervised Learning
D. Semisupervised Learning

4 In PCA, what do the Principal Components represent?

A. The original features sorted by importance
B. The error terms of a regression model
C. The cluster centroids of the data
D. New orthogonal variables that maximize variance

5 Which mathematical concept is central to calculating Principal Components in PCA?

A. Eigenvalues and Eigenvectors
B. Sine and Cosine functions
C. Fourier Transforms
D. Derivatives and Integrals

6 Why is feature scaling (standardization) important before applying PCA?

A. Because PCA is sensitive to the scale of the variables
B. To increase the number of dimensions
C. Because PCA requires categorical data
D. To convert negative numbers to positive

7 What is the relationship between the first principal component and the second principal component?

A. The second is the inverse of the first
B. They are parallel to each other
C. They are identical
D. They are orthogonal (uncorrelated) to each other

8 Which plot is commonly used to determine the optimal number of principal components to retain?

A. Box plot
B. Scatter plot
C. Histogram
D. Scree plot

9 What is the main difference between Feature Selection and Feature Extraction?

A. Feature selection creates new variables; Feature extraction selects a subset
B. Feature selection selects a subset of original variables; Feature extraction creates new variables
C. They are exactly the same
D. Feature selection is for images; Feature extraction is for text

10 If a dataset has 50 variables and you apply PCA to select the top 5 components, what happens to the dimensionality?

A. It becomes 0
B. It reduces to 5
C. It remains 50
D. It increases to 55

11 What is a Feedforward Neural Network?

A. A network that only processes images
B. A network where information moves in only one direction, forward, from the input nodes
C. A network where output is fed back into the input
D. A network that does not use weights

12 What is the fundamental building block of a neural network?

A. Eigenvector
B. Pixel
C. Neuron (Perceptron)
D. Decision Tree

13 In a neural network, what is the role of the 'weight'?

A. To determine the number of layers
B. To represent the strength or importance of a connection between neurons
C. To calculate the accuracy
D. To store the input data

14 What is the purpose of an activation function in a neural network?

A. To sort the input data
B. To reduce the size of the dataset
C. To initialize the weights to zero
D. To introduce non-linearity into the network

15 Which of the following is a commonly used activation function that outputs values between 0 and 1?

A. Linear
B. Sigmoid
C. ReLU (Rectified Linear Unit)
D. Tanh

16 What is a Multi-layer Perceptron (MLP)?

A. A perceptron with no weights
B. A network that only has an input and an output layer
C. A type of PCA
D. A neural network with at least one hidden layer between input and output

17 What limitation of the single-layer perceptron did the MLP solve?

A. It was too slow
B. It could not solve the XOR problem (non-linearly separable data)
C. It required too much memory
D. It could not handle numbers

18 What algorithm is commonly used to train Multi-layer Perceptrons?

A. Principal Component Analysis
B. Apriori Algorithm
C. Backpropagation
D. K-Means Clustering

19 In the context of Backpropagation, what is the role of the 'Loss Function'?

A. To measure the difference between the predicted output and the actual target
B. To increase the speed of the network
C. To randomly assign weights
D. To visualize the neural network

20 What does 'Gradient Descent' do in a neural network?

A. It converts images to text
B. It removes outliers from data
C. It increases the number of neurons
D. It iteratively adjusts weights to minimize the loss function

21 What is an 'Epoch' in neural network training?

A. The initial learning rate
B. The number of hidden layers
C. One forward pass and one backward pass of all the training examples
D. The time it takes to code the model

22 Which activation function is defined as f(x) = max(0, x)?

A. Tanh
B. ReLU (Rectified Linear Unit)
C. Softmax
D. Sigmoid

23 What is the 'Bias' term in a neuron equation?

A. A prejudice in the data
B. The number of inputs
C. The error rate of the model
D. A constant added to the product of inputs and weights to shift the activation function

24 Convolutional Neural Networks (CNNs) are primarily used for which type of data?

A. Time-series financial data
B. Tabular sales data
C. Text sentiment analysis
D. Image and video data

25 What is the core operation in a CNN that allows it to detect features like edges?

A. Flattening
B. Convolution
C. Multiplication
D. Recursion

26 In a CNN, what is a 'Kernel' or 'Filter'?

A. A virus protection software
B. The loss function
C. The output layer of the network
D. A small matrix of weights that slides over the input

27 What is the purpose of 'Pooling' layers (e.g., Max Pooling) in a CNN?

A. To inverse the colors
B. To add color to the image
C. To reduce the spatial dimensions and computation while retaining important features
D. To increase the dimensions of the image

28 What does 'Stride' refer to in the context of CNNs?

A. The size of the pooling window
B. The learning rate
C. The number of filters used
D. The step size the filter moves across the input image

29 What is 'Padding' in a CNN?

A. Adding pixels (usually zeros) around the border of the input image
B. Coloring the image black and white
C. Removing pixels from the image
D. Compressing the image file

30 Before passing the output of convolutional layers to a fully connected dense layer, what operation must be performed?

A. Inverting
B. Rotating
C. Flattening
D. Expanding

31 Recurrent Neural Networks (RNNs) are best suited for what type of data?

A. Sequential data (e.g., time series, text)
B. Static images
C. Independent tabular records
D. Unstructured noise

32 What distinguishes an RNN from a standard Feedforward Neural Network?

A. RNNs have no activation functions
B. RNNs only work on GPUs
C. RNNs cannot have hidden layers
D. RNNs have a feedback loop allowing information to persist

33 In an RNN, what is the 'Hidden State'?

A. A layer that is never trained
B. The final output prediction
C. The memory of the network capturing information about previous steps
D. Data that is deleted after processing

34 What is the 'Vanishing Gradient Problem' commonly faced by standard RNNs?

A. The loss function becomes zero immediately
B. The network runs out of memory
C. Gradients become extremely small, preventing weights from updating effectively in earlier layers
D. Gradients become too large and explode

35 Which architecture was designed specifically to solve the Vanishing Gradient problem in RNNs?

A. CNN (Convolutional Neural Network)
B. PCA (Principal Component Analysis)
C. LSTM (Long Short-Term Memory)
D. Perceptron

36 What is 'Dropout' in the context of Neural Networks?

A. A type of activation function
B. Removing bad data from the input
C. A regularization technique that randomly ignores neurons during training to prevent overfitting
D. Stopping the training early

37 In a multi-class classification problem (e.g., classifying an image as a Cat, Dog, or Bird), which activation function is used in the output layer?

A. Softmax
B. ReLU
C. Sigmoid
D. Linear

38 Which of the following is a hyperparameter in a neural network?

A. Biases
B. Learning Rate
C. Output predictions
D. Weights

39 What does a Learning Rate of 0.001 imply?

A. The model will learn very fast
B. The accuracy will be 0.1%
C. The model has 1000 layers
D. The weights are updated by a small step size in the direction of the gradient

40 In the context of PCA, if the first two principal components explain 95% of the variance, what can be concluded?

A. The other components contain 95% of the information
B. You need at least 10 components
C. The data is useless
D. The data can be effectively reduced to 2 dimensions with minimal information loss

41 Which concept allows a CNN to recognize an object in an image regardless of where it is located in the image?

A. Vanishing Gradient
B. Overfitting
C. Linearity
D. Translation Invariance

42 What is the 'Gate' mechanism in LSTM used for?

A. To connect to the internet
B. To speed up the GPU
C. To calculate the convolution
D. To control what information is added to or removed from the cell state

43 Which of the following is an example of a Many-to-One RNN architecture?

A. Video Frame Prediction
B. Machine Translation (Sequence to Sequence)
C. Image Captioning (Image to Text)
D. Sentiment Analysis (Text to Sentiment Score)

44 In a fully connected (dense) layer, how are neurons connected?

A. Neurons are not connected
B. Randomly connected
C. Each neuron is connected to every neuron in the previous layer
D. Only to the corresponding neuron in the previous layer

45 Why is Non-Linearity important in dimensionality reduction (e.g., t-SNE vs PCA)?

A. It is not important
B. PCA captures linear relationships; non-linear methods capture complex manifolds
C. PCA cannot handle numbers
D. Non-linear methods are always faster

46 What is the primary disadvantage of using a Deep Neural Network compared to a Decision Tree?

A. Cannot handle large data
B. Simplicity
C. Lack of interpretability (Black Box nature)
D. Lower accuracy

47 In PCA, the Covariance Matrix is usually:

A. Undefined
B. Asymmetric
C. Symmetric
D. Diagonal with zeros

48 What is 'Weight Initialization'?

A. Calculating the final weights
B. Setting all weights to 0
C. Setting all weights to 1
D. Setting initial values for weights (often small random numbers) before training

49 Which of the following best describes 'Overfitting' in neural networks?

A. The model performs poorly on training data and test data
B. The model has too few parameters
C. The model performs well on training data but poorly on unseen test data
D. The model performs well on test data but poorly on training data

50 The Universal Approximation Theorem states that:

A. PCA is universal
B. A feedforward network with a single hidden layer can approximate any continuous function
C. A neural network can never reach 100% accuracy
D. CNNs are better than RNNs
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