You are required to build a system that can understand and generate human-like responses. Would you employ AI, Machine Learning, or Deep Learning, and why?
- AI, for its broad capabilities
- Deep Learning, for its capabilities in natural language processing
- Machine Learning, for its predictive models
- nan
Deep Learning, with its advanced neural network structures, is often employed in natural language processing to generate human-like responses.
How can feature scaling affect the performance of certain Machine Learning algorithms?
- It changes the distribution of the data
- It helps algorithms converge faster and perform better
- It increases the computational complexity of the model
- It increases the number of features
Feature scaling normalizes or standardizes the features, making them all on a similar scale. This can help gradient descent-based algorithms converge faster and may lead to better performance for distance-based algorithms like KNN.
You're building a model that is suffering from high variance. Which ensemble method would be more appropriate to use, and why?
- Bagging
- Boosting
- Gradient Boosting
- nan
Bagging is an ensemble method that can reduce high variance by averaging predictions from multiple base learners trained on different subsets of the data. It helps to smooth out the individual variations and enhances the stability of the model.
You implemented L1 regularization to prevent overfitting, but the model's performance did not improve. What could be the reason, and what alternative approach would you try?
- Model is overfitting, try L2 regularization
- Model is overfitting, try increasing regularization
- Model is underfitting, try L2 regularization
- Model is underfitting, try reducing regularization
If the model's performance did not improve with L1 regularization, it might be underfitting, meaning it's too constrained. An alternative approach would be to reduce regularization or try a different form like L2, which might be more suitable.
___________ is a dimensionality reduction technique that maximizes the separability between different classes in a dataset.
- Factor Analysis
- Linear Discriminant Analysis (LDA)
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
Linear Discriminant Analysis (LDA) is used to reduce dimensions while maximizing the separability between different classes, making it particularly useful for classification problems.
How would you handle a scenario where the feature values in a classification problem are on different scales?
- Apply feature scaling techniques like normalization or standardization
- Convert all features to binary values
- Ignore the scales
- Remove features with different scales
Applying feature scaling techniques like normalization or standardization ensures that all feature values are on the same scale. This is crucial for many classification algorithms, as it allows them to perform more effectively and converge faster.
How do features in Machine Learning differ from targets, and why are both necessary?
- Features and targets are the same
- Features are input; Targets are predictions
- Features are predictions; Targets are input
- None of these definitions are correct
Features are the input variables used to make predictions, while targets are the values the model is trying to predict. Both are necessary for supervised learning, where features are used to predict the corresponding targets.
When using PCA, the data must be ___________ before applying the algorithm to ensure that each feature contributes equally.
- clustered
- normalized
- transformed
- visualized
Before applying PCA, the data must be normalized to ensure that each feature contributes equally to the principal components. Normalizing the data means that each feature will have a mean of 0 and a standard deviation of 1, thus ensuring that no feature dominates the others.
How is the number of clusters in K-Means typically determined?
- Based on the dataset size
- Random selection
- Through classification
- Using the Elbow Method
The number of clusters in K-Means is typically determined using the Elbow Method, where the variance is plotted against the number of clusters to find the optimal point.
Can LDA be used for both classification and dimensionality reduction?
- No
- Only for classification
- Only for dimensionality reduction
- Yes
"Yes," LDA can be used both for classification, by finding the best linear combinations of features to separate classes, and for dimensionality reduction, by projecting data into a lower-dimensional space while preserving class separability.
In K-Nearest Neighbors (KNN), the value of K represents the number of __________ considered when making a prediction.
- clusters
- dimensions
- errors
- neighbors
The value of K in KNN refers to the number of neighbors considered when making a prediction.
A model with an AUC value of 1 means it has _________ performance, while an AUC value of 0.5 means the model is performing no better than _________.
- Optimal, Random guessing
- Perfect, Random guessing
- Perfect, a specific threshold
- nan
An AUC value of 1 signifies perfect performance, and the model perfectly separates the classes. An AUC value of 0.5 means the model is performing no better than random guessing and has no discriminative ability between the classes.