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.
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).
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 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.
One of the drawbacks of using t-SNE is that it's not deterministic, meaning multiple runs with the same data can yield ________ results.
- Different
- Identical
- Similar
- Unpredictable
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a probabilistic dimensionality reduction technique. Its non-deterministic nature means that each run may result in a different embedding, making the results unpredictable.
In binary classification, if a model correctly predicts all positive instances and no negative instances as positive, its ________ will be 1.
- Accuracy
- F1 Score
- Precision
- Recall
When a model correctly predicts all positive instances and no negative instances as positive, it means it has perfect "precision." Precision measures how many of the predicted positive instances were correct.
A ________ is a tool in machine learning that helps...
- Feature Extractor
- Principal Component Analysis (PCA)
- Gradient Descent
- Overfitting
Principal Component Analysis (PCA) is a technique used for dimensionality reduction. It identifies and retains important information while reducing the number of input variables in a dataset.
An autoencoder's primary objective is to minimize the difference between the input and the ________.
- Output
- Reconstruction
- Encoding
- Activation
The primary objective of an autoencoder is to minimize the difference between the input and its 'Reconstruction,' which is the encoded-decoded output.
Which regularization technique adds a penalty equivalent to the absolute value of the magnitude of coefficients?
- Elastic Net
- L1 Regularization
- L2 Regularization
- Ridge Regularization
L1 Regularization, also known as Lasso, adds a penalty equivalent to the absolute value of coefficients. This helps in feature selection by encouraging some coefficients to become exactly zero.
Why might it be problematic if a loan approval machine learning model is not transparent and explainable in its decision-making process?
- Increased risk of discrimination
- Enhanced privacy protection
- Improved loan approval process
- Faster decision-making
If a loan approval model is not transparent and explainable, it may lead to increased risks of discrimination, as it becomes unclear why certain applicants were approved or denied loans, potentially violating anti-discrimination laws.