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.
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.
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.
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.
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.
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 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.
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.
In LDA, the goal is to maximize the ___________ variance and minimize the ___________ variance.
- between-class, within-class
- data, features
- features, data
- within-class, between-class
In LDA, the goal is to "maximize the between-class variance and minimize the within-class variance" to find a decision boundary that separates classes.
If the assumptions of Simple Linear Regression are violated, the coefficient estimates may become _________, and predictions may not be reliable.
- Biased
- Efficient
- Improved
- Optimized
If the assumptions of Simple Linear Regression are violated, the coefficient estimates may become biased, leading to unreliable predictions.