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.
What does DBSCAN stand for in the context of clustering algorithms?
- Data-Based Scan Algorithm
- Density-Based Spatial Clustering of Applications with Noise
- Distribution-Based Scan Clustering
- Dynamic-Based Scan Algorithm
DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It's a clustering algorithm that groups together points that are closely packed based on a density function, separating areas where points are concentrated from areas that are sparse or contain noise.
Artificial Intelligence encompasses both ________ and ________, including methods that may not involve learning from data.
- AI, Deep Learning
- Deep Learning, AI
- Machine Learning, AI
- Machine Learning, Deep Learning
Artificial Intelligence encompasses both Machine Learning and Deep Learning, including methods outside of learning from data.
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.
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).
Poor initialization of centroids in K-Means clustering may lead to __________, affecting the quality of the clustering.
- Convergence to global maxima
- Local minima
- Noise
- Overfitting
Poor initialization of centroids can lead the K-Means algorithm to converge to local minima, affecting the quality of the clustering. Local minima occur when the algorithm finds a suboptimal clustering solution.
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.