You need to improve the performance of a weak learner. Which boosting algorithm would you select, and why?
- AdaBoost
- Any boosting algorithm will suffice
- Gradient Boosting without considering the loss function
- Random Boosting
AdaBoost is a boosting algorithm designed to improve the performance of weak learners. By adjusting the weights of misclassified instances and focusing on them in subsequent models, AdaBoost iteratively corrects errors and enhances the overall model's performance.
You are using KNN for a regression problem. What are the special considerations in selecting K and the distance metric, and how would you evaluate the model's performance?
- Choose K and metric considering data characteristics, evaluate using regression metrics
- Choose fixed K and Manhattan metric, evaluate using recall
- Choose large K and any metric, evaluate using accuracy
- Choose small K and Euclidean metric, evaluate using precision
Selecting K and distance metric considering the data characteristics and evaluating the model using regression metrics like RMSE or MAE is the right approach for KNN in regression.
A dataset contains both categorical and numerical features. Which ensemble method might be suitable, and what preprocessing might be required?
- Random Forest with no preprocessing
- Random Forest with normalization
- Random Forest with one-hot encoding
- Random Forest with scaling
Random Forest is an ensemble method suitable for handling both categorical and numerical features. For categorical features, one-hot encoding might be required to convert them into a numerical format that the algorithm can process.
Describe a scenario where Hierarchical Clustering would be more beneficial than K-Means Clustering, and explain the considerations in choosing the linkage method.
- When a fixed number of clusters is required
- When clusters are uniformly distributed
- When clusters have varying sizes and non-spherical shapes
- When computational efficiency is the priority
Hierarchical Clustering is more beneficial than K-Means when clusters have varying sizes and non-spherical shapes. Unlike K-Means, Hierarchical Clustering does not assume spherical clusters and can handle complex structures. The choice of linkage method will depend on the specific characteristics of the clusters, with considerations like distance metric and desired cluster shape guiding the selection.
In reinforcement learning, the agent learns to take actions that maximize the cumulative __________.
- accuracy
- errors
- loss
- rewards
In reinforcement learning, the agent tries to maximize cumulative rewards through its actions.
Machine Learning is commonly used in ____________ to create personalized recommendations.
- Drug Development
- Recommender Systems
- Traffic Management
- Weather Prediction
Machine Learning is extensively used in Recommender Systems to create personalized recommendations, analyzing user behavior and preferences.
If the relationship between variables in a dataset is best fit by a curve rather than a line, you might use _________ regression.
- Linear
- Logistic
- Polynomial
- Ridge
If the relationship between variables is best fit by a curve rather than a line, Polynomial regression would be used. It can model nonlinear relationships by including polynomial terms in the equation.
You have two models with similar Accuracy but different Precision and Recall values. How would you decide which model is better for a given application?
- Choose based on the specific application's needs and tolerance for false positives/negatives
- Choose the one with higher Precision
- Choose the one with higher Recall
- nan
When models have similar Accuracy but different Precision and Recall, the choice between them should be based on the specific application's needs. If false positives are more costly, prioritize Precision; if false negatives are more crucial, prioritize Recall.
In what situations would it be appropriate to use Logistic Regression with the Logit link function?
- All regression problems
- Binary classification with a nonlinear relationship between predictors
- Binary classification with linear relationship between predictors
- Multi-class classification
Logistic Regression with the Logit link function is particularly suited for binary classification problems where there is a linear relationship between the predictors and the log-odds of the response.
One method to mitigate multicollinearity is to apply ___________ regression, which adds a penalty term to the loss function.
- Lasso
- Logistic
- Polynomial
- Ridge
Ridge regression is a technique that can mitigate multicollinearity by adding a penalty term to the loss function. The penalty term helps in reducing the effect of correlated variables, leading to more stable coefficients.