You're working with a dataset where different features are on wildly different scales. How can dimensionality reduction techniques like PCA be adapted to this scenario?
- Apply PCA without any preprocessing
- Ignore the scales
- Scale the features before applying PCA
- Use only large-scale features
When features are on different scales, scaling them before applying PCA is crucial. Standardizing the features ensures that each one contributes equally to the calculation of the principal components, which is vital for the accuracy of the transformation. Ignoring the scales, applying PCA without preprocessing, or focusing only on large-scale features may lead to biased or incorrect results.
What is Machine Learning and why is it important?
- A brand of computer
- A field of AI that learns from experience
- A study of computers
- A type of computer virus
Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without explicit instructions. It's important because it allows systems to learn from data, adapt, and improve over time, making it essential in fields like healthcare, finance, transportation, and more.
You are working on a real-world problem that requires clustering, but the Elbow Method doesn't show a clear elbow point. What might be the underlying issues, and how could you proceed?
- Data doesn't have well-separated clusters; Consider other methods like Silhouette
- Increase the number of data points
- Reduce the number of features
- Use a different clustering algorithm entirely
When the Elbow Method doesn't show a clear elbow point, it may be an indication that the data doesn't have well-separated clusters. In this case, considering other methods like the Silhouette Method to determine the optimal number of clusters is an appropriate course of action.
Explain how a Decision Tree works in the context of Machine Learning.
- Based on complexity, combines data at each node
- Based on distance, groups data at each node
- Based on entropy, splits data at each node
- Based on gradient, organizes data at each node
A Decision Tree works by splitting the data into subsets based on feature values. This is done recursively at each node by selecting the feature that provides the best split according to a metric like entropy or Gini impurity. The process continues until specific criteria are met, creating a tree-like structure.
When it comes to classifying data points, the _________ algorithm considers the 'K' closest points to make a decision.
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Random Forest
- Support Vector Machines
K-Nearest Neighbors (KNN) algorithm classifies a data point based on the majority class of its 'K' closest points in the dataset, using distance metrics to determine proximity.
The risk of overfitting can be increased if the same data is used for both _________ and _________ of the Machine Learning model.
- evaluation, processing
- training, testing
- training, validation
- validation, training
If the same data is used for both "training" and "testing," the model may perform well on that data but poorly on unseen data, leading to overfitting.
To detect multicollinearity in a dataset, one common method is to calculate the ___________ Inflation Factor (VIF).
- Validation
- Variable
- Variance
- Vector
The Variance Inflation Factor (VIF) is a measure used to detect multicollinearity. It quantifies how much a variable is inflating the standard errors due to its correlation with other variables. A high VIF indicates multicollinearity.
_________ clustering builds a tree-like diagram called a dendrogram, allowing you to visualize the relationships between clusters.
- DBSCAN
- Hierarchical
- K-Means
- Spectral
Hierarchical clustering builds a dendrogram, which allows visualization of the relationships between clusters, showing how the clusters are connected.
How does LDA maximize the separation between different classes in a dataset?
- By maximizing between-class variance and minimizing within-class variance
- By maximizing both within-class and between-class variance
- By minimizing between-class variance and maximizing within-class variance
- By minimizing both within-class and between-class variance
LDA maximizes the separation between different classes by "maximizing between-class variance and minimizing within-class variance." This process ensures that different classes are far apart, while data points within the same class are close together, resulting in better class separation.
You reduced the complexity of your model to prevent overfitting, but it led to underfitting. How would you find a balance between complexity and fit?
- Add regularization
- All of the above
- Increase dataset size
- Try cross-validation
Finding a balance might involve using cross-validation to systematically find the right level of complexity that fits well with the training data but also generalizes well to the validation data. This process helps in finding the right hyperparameters without biasing the test data.