Vitality by Design Interview Questions and Answers

To prevent overfitting, I used techniques like regularization, cross-validation, and early stopping. For underfitting, I tried increasing model complexity and adding more features.

• Used regularization techniques like L1 and L2 regularization to penalize large weights

• Used cross-validation to evaluate model performance on different subsets of data

• Used early stopping to prevent the model from continuing to train when performance on validation set stops improving

• For underfitting, ...read more

The model/approach was chosen based on its accuracy, interpretability, and scalability.

• The chosen model/approach had the highest accuracy compared to others.

• The chosen model/approach was more interpretable and easier to explain to stakeholders.

• The chosen model/approach was more scalable and could handle larger datasets.

• Other models/approaches were considered but did not meet the requirements or had limitations.

• The chosen model/approach was also more suitable for the specific ...read more

I used a combination of supervised and unsupervised learning approaches to analyze the data.

• I used supervised learning to train models for classification and regression tasks.

• I used unsupervised learning to identify patterns and relationships in the data.

• I also used feature engineering to extract relevant features from the data.

• I chose this approach because it allowed me to gain insights from the data and make predictions based on it.

Chi square distribution is a probability distribution used in statistical tests to determine the significance of relationships between categorical variables.

• Chi square distribution is a continuous probability distribution that is used in statistical tests such as the chi square test.

• It is skewed to the right and its shape is determined by the degrees of freedom.

• Assumptions involved in chi square distribution include: random sampling, independence of observations, and expected...read more

Clustering and bagging boosting algorithms are popular techniques in machine learning for grouping data points and improving model accuracy.

• Clustering algorithms like K-means, DBSCAN, and hierarchical clustering are used to group similar data points together based on certain criteria.

• Bagging algorithms like Random Forest create multiple subsets of the training data and train individual models on each subset, then combine their predictions to improve accuracy.

• Boosting algorith...read more

Clustering algorithms group similar data points together based on certain criteria.

• K-means: partitions data into K clusters based on centroids

• Hierarchical clustering: creates a tree of clusters

• DBSCAN: density-based clustering algorithm

• Mean Shift: shifts centroids to maximize data points within a certain radius

• Gaussian Mixture Models: assumes data points are generated from a mixture of Gaussian distributions