________ is a type of classification where there are more than two classes.
- Binary classification
- Imbalanced classification
- Multiclass classification
- Overfitting
Multiclass classification refers to the classification problems where there are more than two classes to be predicted. This contrasts with binary classification, which involves just two classes.
You are working with a dataset containing many irrelevant features. Which regularization technique would you prefer and why?
- ElasticNet
- Lasso
- Ridge
- nan
Lasso regularization adds an L1 penalty, which can cause some coefficients to be exactly zero, effectively removing irrelevant features from the model.
What is the primary goal of Machine Learning?
- Data cleaning
- Data prediction and generalization
- Data storage
- Data visualization
The primary goal of Machine Learning is to build models that can predict and generalize from data, making decisions or predictions based on input data.
Can you explain the concept of 'density reachability' in clustering?
- Based on Hierarchical Structure
- Based on Number of Clusters
- Defines How Points Are Connected Through Density
- Defines How Points Are Directly Connected
Density reachability in clustering refers to how points are connected through density, meaning one point is density-reachable from another if there's a sequence of points connecting them within a given density threshold.
In what way does Machine Learning support the pharmaceutical industry in drug discovery and development?
- Drug Discovery and Development
- Image Recognition
- Marketing Strategies
- Supply Chain Management
Machine Learning supports the pharmaceutical industry by analyzing biological data to predict potential drug interactions, identifying promising compounds, enhancing drug design, and accelerating the overall drug discovery and development process.
You're clustering a large dataset, and computational efficiency is a concern. Which clustering techniques might be more suitable, and why?
- DBSCAN
- Hierarchical Clustering
- K-Means
- K-Means and DBSCAN
Both K-Means and DBSCAN offer good computational efficiency, making them suitable for handling large datasets. K-Means, in particular, can be implemented with scalable variations like Mini-Batch K-Means.
A company wants to predict customer churn based on historical data. What considerations must be made in selecting and tuning a Machine Learning model for this task?
- Considering the business context, available data, model interpretability, and performance metrics
- Focusing only on accuracy
- Ignoring feature engineering
- Selecting the most complex model available
Predicting customer churn requires understanding the business context, the nature of the data, and the need for model interpretability. Metrics such as precision, recall, and F1-score might be more relevant than mere accuracy.
You're building a recommendation system without access to labeled data. How would you proceed using unsupervised learning techniques?
- Combining labeled and unlabeled data
- Employing labeled data
- Using clustering methods
- Using reinforcement strategies
Clustering methods are a common approach in Unsupervised Learning to group data based on similarities, suitable for recommendation systems without labeled data.
What is the primary purpose of using Cross-Validation in Machine Learning?
- To enhance the model's complexity
- To estimate the model's performance on unseen data
- To increase the training speed
- To select optimal hyperparameters
Cross-Validation's primary purpose is to estimate the model's performance on unseen data by dividing the dataset into training and validation sets. It provides a more reliable evaluation than using a single static validation set.
You've detected a high Variance Inflation Factor (VIF) for one of the variables in your Multiple Linear Regression model. What does this indicate, and how would you proceed?
- High multicollinearity and consider removing or combining variables
- Low multicollinearity
- No multicollinearity
- The variable is not significant
A high VIF indicates high multicollinearity, meaning the variable is highly correlated with other variables in the model. You may consider removing or combining variables, applying regularization, or using dimensionality reduction techniques to address this issue and improve the model's performance.
In DBSCAN, what does the term 'Epsilon' refer to?
- Edge Distance
- Error Rate
- Estimated Density
- Maximum Radius of the Neighborhood
In DBSCAN, 'Epsilon' refers to the maximum radius of the neighborhood around a data point. If there are enough points within this radius (defined by MinPts), the point is considered a core point, leading to the formation of a cluster. It's a critical parameter affecting the clustering result, controlling how close points must be to form a cluster.
How would you handle a situation in which the SVM is performing poorly due to the choice of kernel?
- Change the dataset
- Change to a more appropriate kernel using cross-validation
- Ignore the issue
- Use only linear kernel
Changing to an appropriate kernel using cross-validation can enhance the performance if the current kernel is not suitable for the data.