Which type of Machine Learning algorithm would be best suited for predicting a continuous value?
- Classification
- Clustering
- Regression
- Reinforcement Learning
Regression algorithms are designed to predict continuous values, such as stock prices or temperatures, by learning the relationship between independent and dependent variables.
You're designing a system for image recognition with a need for real-time response. Which approach would be more appropriate: Machine Learning or Deep Learning, and why?
- Both are equally appropriate
- Deep Learning, for its advanced image recognition capabilities
- Machine Learning, for its simpler models
- nan
Deep Learning, particularly Convolutional Neural Networks (CNNs), is highly effective for image recognition and is usually preferred for such tasks.
In a marketing campaign, you want to predict the likelihood of a customer buying a product. How might the Odds Ratio be useful in interpreting the effect of different variables?
- By quantifying the correlation between variables
- By quantifying the effect of variables on the odds of buying
- By quantifying the effect of variables on the probability of buying
- By quantifying the relationship between input variables
The Odds Ratio can be useful in interpreting the effect of different variables on the odds of buying, allowing marketers to understand which factors have the most significant impact on purchase likelihood.
What are the limitations of using R-Squared as the sole metric for evaluating the goodness of fit in a regression model?
- R-Squared always increases with more predictors; doesn't account for bias
- R-Squared always increases with more predictors; doesn't penalize complexity in the model
- R-Squared is sensitive to outliers; doesn't consider the number of predictors
- R-Squared provides absolute error values; not suitable for non-linear models
One major limitation of R-Squared is that it always increases with the addition of more predictors, regardless of whether they are relevant. This can lead to overly complex models that don't generalize well. R-Squared doesn't penalize for complexity in the model, making it possible to achieve a high R-Squared value with an overfitted model. It might not always be the best sole metric for assessing the goodness of fit.
Can you explain the impact of regularization strength on the coefficients in ElasticNet?
- Decreases coefficients proportionally
- Increases coefficients
- No impact
- Varies based on L1/L2 ratio
ElasticNet combines L1 and L2 penalties, so the impact on coefficients depends on the balance between L1 and L2, controlled by the hyperparameters.
You've applied K-Means clustering, but the results are inconsistent across different runs. What could be the issue, and how would you address it?
- Change Number of Clusters
- Increase Dataset Size
- Initialize Centroids Differently
- Use Different Distance Metric
K-Means clustering can be sensitive to initial centroid placement. Trying different initialization strategies can lead to more consistent results.
You have a dataset with a high degree of multicollinearity. What steps would you take to address this before building a Multiple Linear Regression model?
- Apply feature selection or dimensionality reduction techniques
- Ignore it
- Increase the size of the dataset
- Remove all correlated variables
Multicollinearity can be addressed by applying feature selection techniques like LASSO or using dimensionality reduction methods like Principal Component Analysis (PCA). These techniques help in removing or combining correlated variables, reducing multicollinearity and improving the model's stability.
Dimensionality reduction is often used to overcome the ___________ problem, where having too many features relative to the number of observations can lead to overfitting.
- curse of dimensionality
- multicollinearity
- overfitting
- scaling
The overfitting problem occurs when a model is too complex relative to the amount and noise of the data, which can happen when there are too many features. Dimensionality reduction techniques can help by simplifying the feature space, reducing the risk of overfitting.
Can you explain the concept of feature importance in Random Forest?
- Feature importance focuses on eliminating features
- Feature importance is irrelevant in Random Forest
- Feature importance quantifies the contribution of each feature to the model's predictions
- Feature importance ranks the features by their correlation with the target
Feature importance in Random Forest quantifies the contribution of each feature to the model's predictions. It's based on the average impurity decrease computed from all decision trees in the forest. This helps in understanding the relative importance of different features in the model.
What is the primary function of the hyperparameters in SVM?
- Compression
- Controlling complexity and margin
- Data Cleaning
- Visualization
Hyperparameters in SVM are used to control the complexity of the model and the margin between classes.