The disadvantage of LASSO regression for feature selection compared to Ridge and Elastic Net regression is?

LASSO is unable to handle multicollinearity effectively

Which mathematical operations is more appropriate for modeling an interaction between two categorical features?

Creating a new categorical feature representing all possible combinations

Which of the following best describes the primary goal of reinforcement learning?

Maximize the cumulative reward through interaction with the environment

Which dimensionality reduction techniques is a linear technique that maximizes the variance of the projected data?

Principal Component Analysis (PCA)

Independent Component Analysis (ICA) is particularly useful for separating:

Statistically independent components in a dataset

Only captures linear relationships between features and the target variable

Which of the following challenges is specific to applying reinforcement learning in finance?

Financial market is often non-stationary, with changing market conditions

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Chapter 5B

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Questions 12

Answers

1
Which of the following ensemble methods can be parallelized, allowing for faster training and prediction times?
Bagging
Stacking
Boosting
Both a) and c)
2
The disadvantage of LASSO regression for feature selection compared to Ridge and Elastic Net regression is?
LASSO does not perform well with a large number of features
LASSO is unable to handle multicollinearity effectively
LASSO is more computationally intensive
LASSO is sensitive to the presence of outliers in the dataset
3
Which dimensionality reduction technique can learn non-linear relationship between features and based on neural network?
Linear Discriminant Analysis (LDA)
Independent Component Analysis (ICA)
Principal Component Analysis (PCA)
Autoencoders
4
Which of the following factor contributes to the high computational cost and difficult-to-train issue in DL?
All of the above
The need for large amounts of training data
The large number of model parameters
The use of backpropagation and gradient descent for optimization
5
Which mathematical operations is more appropriate for modeling an interaction between two categorical features?
Creating a new categorical feature representing all possible combinations
Addition
Multiplication
Subtraction
6
Which DL architectures is designed to address the vanishing gradient problem often encountered when training RNN?
Long Short-Term Memory (LSTM)
Gated Recurrent Units (GRU)
Deep Convolutional Neural Networks (DCNNs)
Transformer
7
Which of the following best describes the primary goal of reinforcement learning?
Clustering data points based on similarity
Reducing the dimensionality of input data
Minimizing the error between predicted and actual outputs
Maximize the cumulative reward through interaction with the environment
8
Which dimensionality reduction techniques is a linear technique that maximizes the variance of the projected data?
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Independent Component Analysis (ICA)
9
Question
Pearson correlation coefficient
Mutual information
Principal component analysis
Variance threshold
10
Independent Component Analysis (ICA) is particularly useful for separating:
High-dimensional data into a lower-dimensional representation
Statistically independent components in a dataset
Non-linear relationships in a dataset
Uncorrelated components in a dataset
11
Question
All of the above
It is sensitive to the presence of outliers in the dataset
It does not account for interactions between features
Only captures linear relationships between features and the target variable
12
Which of the following challenges is specific to applying reinforcement learning in finance?
Financial data is discrete and requires specialized algorithms
Financial market is often non-stationary, with changing market conditions
Financial data is usually high-dimensional and requires preprocessing
Financial data is often noisy and contains missing values

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