Explain the role of Machine Learning in optimizing supply chain and inventory management.
- Customer Segmentation
- Image Recognition
- Sentiment Analysis
- Supply Chain Optimization
Machine Learning plays a vital role in supply chain optimization by analyzing and predicting demand, improving inventory management, optimizing logistics, and enhancing decision-making through predictive analytics.
Explain how the learning agent interacts with the environment in Reinforcement Learning.
- Through clustering
- Through combining labeled and unlabeled data
- Through labeled data
- Through rewards and penalties
The agent in Reinforcement Learning interacts with the environment through rewards and penalties, learning to take actions to achieve a goal.
What is the difference between Gradient Boosting and AdaBoost?
- Both are the same
- Both focus on increasing bias
- Gradient Boosting is for regression, AdaBoost is for classification
- Gradient Boosting uses gradients to correct errors, while AdaBoost focuses on weighting misclassified instances
Gradient Boosting builds models sequentially, using gradients to correct errors and minimize the loss function. AdaBoost, on the other hand, adjusts the weights of misclassified instances to focus the next model on them. Both aim to improve performance but use different approaches.
What role does the regularization parameter 'C' play in an SVM?
- Controls balance between margin maximization and error minimization
- Controls visualization
- Increases computation time
- Reduces data size
The regularization parameter 'C' in SVM controls the trade-off between maximizing the margin and minimizing classification errors.
How is Recall defined in classification, and when is it an important metric to consider?
- False Positives / Total predictions
- True Negatives / (True Negatives + False Positives)
- True Positives / (True Positives + False Negatives)
- True Positives / (True Positives + False Positives)
Recall is the ratio of true positive predictions to the sum of true positives and false negatives. It measures the ability to correctly identify all relevant instances and is crucial when the cost of false negatives is high, such as in medical diagnoses.
Explain the role of eigenvalues and eigenvectors in PCA.
- Eigenvalues represent direction, eigenvectors variance
- Eigenvalues represent variance, eigenvectors direction
- Neither plays a role in PCA
- They are used in LDA, not PCA
In PCA, eigenvectors represent the directions in which the data varies the most, while the corresponding eigenvalues give the amount of variance in those directions. These are obtained from the covariance matrix of the original data, and the eigenvectors with the largest eigenvalues become the principal components that capture the most significant patterns in the data.
What is the mathematical relationship between Eigenvalues and Eigenvectors in PCA?
- Eigenvalues are scalar multiples of eigenvectors
- They are inversely related
- They are the same
- They are unrelated
In PCA, eigenvalues and eigenvectors have a mathematical relationship where the eigenvalues are scalar multiples of the eigenvectors. They form the eigenvalue-eigenvector equation for the covariance matrix.
What could be the possible consequence of choosing a very small value of K in the KNN algorithm?
- Increased efficiency
- Overfitting
- Reduced complexity
- Underfitting
Choosing a very small value of K in the KNN algorithm can lead to overfitting, where the model becomes too sensitive to noise in the training data.
What type of problems is Logistic Regression mainly used to solve?
- Binary classification problems
- Clustering problems
- Regression problems
- Unsupervised learning problems
Logistic Regression is mainly used to solve binary classification problems, where the goal is to classify instances into one of two classes.
When interpreting a dendrogram in Hierarchical Clustering, the height of the _________ represents the distance at which clusters are merged.
- Branches
- Leaves
- Lines
- Nodes
In a dendrogram, the height of the branches represents the distance at which clusters are merged. The higher the branch, the greater the distance, indicating that the clusters being merged are less similar. This information can guide the selection of the number of clusters and provides insights into the underlying structure of the data.