In ElasticNet regularization, the mixing parameter 'alpha' balances the effects of ________ and ________.
- L1, L2
- L1, L3
- L2, L3
- nan
The 'alpha' parameter in ElasticNet regularization balances the effects of L1 and L2 penalties, providing a compromise between Ridge and Lasso.
The process of fine-tuning a Machine Learning model by changing its settings or _________ is vital for achieving optimal performance.
- Algorithms
- Features
- Hyperparameters
- Targets
Hyperparameters are the settings or parameters of a machine learning model that are defined prior to training and are fine-tuned to optimize performance.
In the context of a Confusion Matrix, _________ represents the cases where the model correctly predicted the negative class.
- False Negatives
- False Positives
- True Negatives
- True Positives
True Negatives (TN) in a Confusion Matrix represent cases where the model correctly predicted the negative class. It indicates that the negative instances were classified correctly.
The _________ hyperplane in SVM maximizes the margin between the support vectors of different classes.
- Decision
- Fixed
- Optimal
- Random
The optimal hyperplane in SVM is the one that maximizes the margin between support vectors of different classes.
Hierarchical Clustering can be either agglomerative, where clusters are built from the bottom up, or divisive, where clusters are split from the top down. The most common method used is _________.
- Agglomerative
- Complete Linkage
- Divisive
- Single Linkage
Agglomerative method is the most commonly used approach in Hierarchical Clustering. It builds clusters from the bottom up, starting with individual data points and merging them into progressively larger clusters. This method allows for the creation of a dendrogram, which can be analyzed to choose the optimal number of clusters and understand the hierarchical relationships within the data.
How does the Kernel Trick help in SVM?
- Enhances data visualization
- Reduces data size
- Speeds up computation
- Transforms data into higher dimension
The Kernel Trick in SVM transforms the data into a higher-dimensional space to make it linearly separable.
Why is the choice of distance metric significant in the K-Nearest Neighbors (KNN) algorithm?
- It affects clustering efficiency
- It defines the complexity of the model
- It determines the similarity measure
- It influences feature selection
The choice of distance metric in KNN significantly impacts how similarity between instances is measured, affecting the neighbors chosen.
What is an interaction effect in Multiple Linear Regression?
- A combined effect of two variables
- Linear relationship between variables
- Model optimization
- Removing irrelevant features
An interaction effect occurs when the effect of one variable on the dependent variable depends on the level of another variable. It shows the combined effect.
Your Logistic Regression model is suffering from separation, causing some estimated Odds Ratios to be extremely large. How could you handle this issue?
- By adding more variables
- By applying regularization techniques
- By increasing the size of the dataset
- By removing all predictors
Separation in Logistic Regression can lead to overly large coefficient estimates. Applying regularization techniques, such as Ridge or Lasso, can help in constraining the coefficient estimates and mitigate this issue.
In which type of Machine Learning does the model learn from labeled data?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Supervised Learning involves training a model on a labeled dataset, meaning the desired output for each input is known. The model learns to predict the output from the input data.