What could be the potential problems if the assumptions of Simple Linear Regression are not met?
- Model May Become Biased or Inefficient
- Model May Overfit
- Model Will Always Fail
- No Impact on Model
If the assumptions of Simple Linear Regression are not met, the model may become biased or inefficient, leading to unreliable estimates. It may also affect the validity of statistical tests.
Ridge regularization adds a ________ penalty to the loss function, which helps to constrain the coefficients.
- L1
- L1 and L2
- L2
- nan
Ridge regularization adds an L2 penalty to the loss function, which helps to reduce the coefficients' magnitude without setting them to zero.
Imagine you are working with a large dataset, and the Elbow Method is computationally expensive. What alternative methods might you consider for determining the number of clusters?
- Double the number of centroids
- Gap Statistic, Silhouette Method
- Randomly choose the number of clusters
- Use the Elbow Method with reduced data
Alternatives like the Gap Statistic and Silhouette Method are used to determine the optimal number of clusters when the Elbow Method is computationally expensive. These methods consider cluster cohesion and separation without requiring extensive computations.
In a scenario where the targets are imbalanced, how would this affect the training and testing process, and what strategies would you apply to handle it?
- Apply resampling techniques
- Focus on specific evaluation metrics
- Ignore the imbalance
- Use only the majority class
Imbalanced targets can bias the model towards the majority class, leading to poor performance on the minority class. Applying resampling techniques like oversampling the minority class or undersampling the majority class balances the data. This, combined with using appropriate evaluation metrics like precision, recall, or F1 score, ensures that the model is more sensitive to the minority class.
The _________ linkage method in Hierarchical Clustering minimizes the variance of the distances between clusters.
- Average Linkage
- Complete Linkage
- Single Linkage
- Ward's Method
Ward's Method minimizes the variance of the distances between clusters. It considers the sum of squared deviations from the mean and tends to create equally sized clusters. This method can be beneficial when we want compact, spherical clusters and when minimizing within-cluster variance is a primary consideration.
What are the advantages and limitations of using Bootstrapping in Machine Learning?
- Fast computation but lacks precision
- Reduced bias but increased computation complexity
- Robust statistical estimates but can introduce high variance
- Robust statistical estimates but may not always be appropriate for all data types
The advantages of Bootstrapping include robust statistical estimates, even with small samples, by resampling with replacement. However, it may not always be appropriate for all data types, especially if the underlying distribution of the data is not well represented by resampling. It provides valuable insights but needs to be applied considering the nature of the data and problem.
While R-Squared describes the proportion of variance explained by the model, ________ adjusts this value based on the number of predictors, providing a more nuanced understanding of the model's fit.
- Adjusted R-Squared
- MSE
- R-Squared
- RMSE
Adjusted R-Squared is an extension of R-Squared that adjusts the value based on the number of predictors in the model. While R-Squared describes the proportion of variance explained by the model, Adjusted R-Squared takes into account the complexity of the model by considering the number of predictors. This leads to a more nuanced understanding of the model's fit, particularly when comparing models with different numbers of predictors.
You are working on a binary classification problem, and your model is consistently predicting the majority class. What could be causing this issue and how would you approach resolving it?
- Data is corrupted; clean the data
- Ignoring the minority class; use resampling techniques
- Incorrect algorithm; change algorithm
- Too many features; perform feature selection
The issue could be due to imbalanced classes. Approaching it by using resampling techniques, such as oversampling the minority class or undersampling the majority class, can help balance the classes and improve the model's performance.
Increasing the regularization parameter in Ridge regression will ________ the coefficients but will not set them to zero.
- Decrease
- Increase
- Maintain
- nan
Increasing the regularization parameter in Ridge regression will shrink the coefficients towards zero but will not set them to zero, due to the L2 penalty.
Balancing the _________ in a training dataset is vital to ensure that the model does not become biased towards one particular outcome.
- classes
- features
- models
- parameters
Balancing the "classes" in a training dataset ensures that the model does not become biased towards one class, leading to a more accurate and fair representation of the data. This is especially crucial in classification tasks.