In comparison to PCA, LDA focuses on maximizing the separability between different ___________ rather than the variance of the data.
- classes
- features
- principal components
- variables
Unlike PCA, which focuses on the variance of the data, LDA emphasizes maximizing the separability between "different classes."
How does the Kernel Trick help in dealing with non-linear data in SVM?
- Enhances data visualization
- Maps data into higher-dimensional space for linear separation
- Reduces data size
- Speeds up computation
The Kernel Trick helps in dealing with non-linear data by mapping it into a higher-dimensional space where it can be linearly separated.
What is the impact of pruning on the bias-variance tradeoff in a Decision Tree model?
- Increases bias, reduces variance
- Increases both bias and variance
- Reduces bias, increases variance
- Reduces both bias and variance
Pruning a Decision Tree leads to a simpler model, which can increase bias but reduce variance. This tradeoff helps to avoid overfitting the training data and often results in a model that generalizes better to unseen data.
What is the role of a decision boundary in classification problems?
- Separating classes in the feature space
- Separating data into clusters
- Separating features
- Separating training and test data
A decision boundary is a hypersurface that partitions the underlying feature space into classes. It plays a crucial role in determining the class label of a new data point based on which side of the boundary it lies.
Why is Bootstrapping an essential technique in statistical analysis?
- It allows training deep learning models
- It enables the estimation of the distribution of a statistic
- It provides a method for feature selection
- It speeds up computation
Bootstrapping is essential in statistical analysis because it allows estimating the distribution of a statistic, even with a small sample. By repeatedly resampling with replacement, it creates numerous "bootstrap samples," enabling the calculation of standard errors, confidence intervals, and other statistical properties.
Explain how the F1-Score is computed and why it is used.
- Arithmetic mean of Precision and Recall, balances both metrics
- Geometric mean of Precision and Recall, emphasizes Recall
- Harmonic mean of Precision and Recall, balances both metrics
- nan
F1-Score is the harmonic mean of Precision and Recall. It helps balance both metrics, particularly when there's an uneven class distribution. It's often used when both false positives and false negatives are important to minimize.
What term is used to refer to a set of input variables and their corresponding target values used to evaluate a Machine Learning model's performance?
- Evaluation set
- Testing set
- Training set
- Validation set
The "Testing set" is a set of input variables and corresponding target values used to evaluate a Machine Learning model's performance. It helps in assessing how well the model will perform on unseen data.
What is regression in the context of Machine Learning?
- A method for classification
- A method for clustering
- A method for predicting a continuous output
- A method for text analysis
Regression in Machine Learning is a supervised learning technique used to predict a continuous output or numerical value based on input variables.
In a Multiple Linear Regression model, you discovered a significant interaction effect between two variables. How would you interpret this finding, and what implications might it have for the model?
- Add more variables
- Ignore the interaction
- No change to the model
- The effect of one variable depends on the level of the other
A significant interaction effect indicates that the effect of one variable on the response depends on the level of another variable. This means that the relationship between variables is not simply additive, and it may require the inclusion of an interaction term in the model to capture this complex relationship accurately.
The F1-Score is the harmonic mean of _________ and _________.
- Accuracy, Recall
- Precision, Recall
- Precision, Specificity
- nan
The F1-Score is the harmonic mean of Precision and Recall. It gives equal weight to both these metrics, providing a balance between the ability to correctly identify positive cases and avoid false positives.
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 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.