If you're working with high-dimensional data and you want to reduce its dimensionality for visualization without necessarily preserving the global structure, which method would be apt?
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Independent Component Analysis (ICA)
When you want to reduce high-dimensional data for visualization without preserving global structure, t-SNE is apt. It focuses on local similarities, making it effective for revealing clusters and patterns in the data, even if the global structure is not preserved.
To avoid overfitting in large neural networks, one might employ a technique known as ________, which involves dropping out random neurons during training.
- Batch Normalization
- L2 Regularization
- Gradient Descent
- Dropout
The 'Dropout' technique involves randomly deactivating a fraction of neurons during training, which helps prevent overfitting in large neural networks.
In a case where a company wants to detect abnormal patterns in vast amounts of transaction data, which type of neural network model would be particularly beneficial in identifying these anomalies based on data reconstructions?
- Variational Autoencoder
- Long Short-Term Memory (LSTM)
- Feedforward Neural Network
- Restricted Boltzmann Machine
Variational Autoencoders (VAEs) are excellent for anomaly detection because they model data distributions and can recognize deviations from these distributions.
How do residuals, the differences between the observed and predicted values, relate to linear regression?
- They are not relevant in linear regression
- They indicate how well the model fits the data
- They measure the strength of the relationship between predictors
- They represent the sum of squared errors
Residuals in linear regression measure how well the model fits the data. Specifically, they represent the differences between the observed and predicted values. Smaller residuals indicate a better fit, while larger residuals suggest a poorer fit.
In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- Categorical Data
- High-Dimensional Data
- Numerical Data
- Time Series Data
Naive Bayes works well in text classification because it can effectively handle high-dimensional data with numerous features (words or terms).
A data scientist notices that their model performs exceptionally well on the training set but poorly on the validation set. What might be the reason, and what can be a potential solution?
- Data preprocessing is the reason, and fine-tuning hyperparameters can be a potential solution.
- Overfitting is the reason, and regularization techniques can be a potential solution.
- The model is working correctly, and no action is needed.
- Underfitting is the reason, and collecting more data can be a potential solution.
Overfitting occurs when the model learns the training data too well, leading to poor generalization. Regularization techniques like L1 or L2 regularization can prevent overfitting by adding penalties to the model's complexity, helping it perform better on the validation set.
Which type of machine learning is primarily concerned...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
In supervised learning, the model is trained using labeled data, where input features are associated with known output labels. It learns to make predictions based on this labeled data.
Why is feature selection important in building machine learning models?
- All of the Above
- Enhances Model Interpretability
- Reduces Overfitting
- Speeds up Training
Feature selection is important for various reasons. It reduces overfitting by focusing on relevant features, speeds up training by working with fewer features, and enhances model interpretability by highlighting the most important factors affecting predictions.
What distinguishes autoencoders from other traditional neural networks in terms of their architecture?
- Autoencoders have an encoder and decoder
- Autoencoders use convolutional layers
- Autoencoders have more hidden layers
- Autoencoders don't use activation functions
Autoencoders have a distinct encoder-decoder architecture, enabling them to learn efficient representations of data and perform tasks like image denoising and compression.
Deep Q Networks (DQNs) are a combination of Q-learning and what other machine learning approach?
- Convolutional Neural Networks
- Recurrent Neural Networks
- Supervised Learning
- Unsupervised Learning
Deep Q Networks (DQNs) combine Q-learning with Convolutional Neural Networks (CNNs) to handle complex and high-dimensional state spaces.
For the k-NN algorithm, what could be a potential drawback of using a very large value of kk?
- Increased Model Bias
- Increased Model Variance
- Overfitting to Noise
- Slower Training Time
A potential drawback of using a large value of 'k' in k-NN is that it can overfit to noise in the data, leading to reduced accuracy on the test data.
You have a dataset with numerous features, and you suspect that many of them are correlated. Using which technique can you both reduce the dimensionality and tackle multicollinearity?
- Data Imputation
- Decision Trees
- Feature Scaling
- Principal Component Analysis (PCA)
Principal Component Analysis (PCA) can reduce dimensionality by transforming correlated features into a smaller set of uncorrelated variables. It addresses multicollinearity by creating new axes (principal components) where the original variables are no longer correlated, thus improving the model's stability and interpretability.