What is the main function of the Gini Index in a Decision Tree?
- Determine Leaf Nodes
- Increase Complexity
- Measure Purity
- Reduce Overfitting
The Gini Index measures the impurity or purity of a split in the Decision Tree.
Your regression model's MSE is high, but the MAE is relatively low. What might this indicate about the model's error distribution, and how would you investigate further?
- Model has consistent errors; needs more training
- Model has frequent large errors; needs regularization
- Model has many small errors, but some significant outliers; analyze residuals
- Model is perfect; no further investigation required
A high Mean Squared Error (MSE) with a relatively low Mean Absolute Error (MAE) indicates that the model likely has many small errors but also some significant outliers. The squaring in MSE amplifies the effect of these outliers. Analyzing the residuals (the differences between predicted and actual values) can help to understand the nature of these errors and possibly guide improvements in the model.
___________ is a popular method for dimensionality reduction that transforms the data into a new coordinate system where the variance is maximized.
- Feature Selection
- Linear Discriminant Analysis
- Principal Component Analysis
- t-SNE
Principal Component Analysis (PCA) is a method that transforms data into a new coordinate system where the variance is maximized. It's a popular technique for reducing dimensions while preserving as much information as possible in the reduced space.
Can you explain the concept of Semi-Supervised Learning and how it bridges the gap between supervised and unsupervised learning?
- Combines labeled & unlabeled data
- Uses only labeled data
- Uses only unlabeled data
- Uses rewards and penalties
Semi-Supervised Learning bridges the gap by combining both labeled and unlabeled data, utilizing strengths of both supervised and unsupervised.
Cross-Validation divides the dataset into "k" subsets, or _______, where one subset is used as the validation set, and the rest are used for training.
- clusters
- folds
- groups
- partitions
Cross-Validation involves dividing the dataset into "k" subsets, referred to as "folds." One fold is used as the validation set, while the remaining are used for training. This process is repeated k times, with each fold being used exactly once as the validation set.
In Machine Learning, the term _________ refers to the values that the algorithm tries to predict, while _________ refers to the input variables.
- data, parameters
- features, targets
- parameters, data
- targets, features
In machine learning, "targets" are the values that a model tries to predict based on given "features," which are the input variables that represent the data.
How does Principal Component Analysis (PCA) work as a method of dimensionality reduction?
- By classifying features
- By maximizing variance
- By minimizing variance
- By selecting principal features
Principal Component Analysis (PCA) works by transforming the original features into a new set of uncorrelated features called principal components. It does so by maximizing the variance along these new axes, meaning that the first principal component explains the most variance, the second explains the second most, and so on.
What are some common challenges in high-dimensional data that dimensionality reduction aims to address?
- All of the above
- Computational efficiency
- Curse of dimensionality
- Overfitting
Dimensionality reduction aims to address several challenges in high-dimensional data, including the curse of dimensionality (where distance measures lose meaning), overfitting (where models fit noise), and computational efficiency (since fewer dimensions require less computing resources).
Interaction effects in Multiple Linear Regression can be represented by adding a ___________ term for the interacting variables.
- additive
- divided
- multiplied
- subtractive
Interaction effects are represented by adding a multiplied term for the interacting variables in the model. It captures the combined effect that is not simply additive and reflects how the response variable changes when both interacting variables change together.
In Polynomial Regression, a higher degree can lead to ________, where the model learns the noise in the data.
- accuracy
- overfitting
- stability
- underfitting
A higher degree in Polynomial Regression may cause the model to fit the noise in the data, leading to overfitting.