Clarivate Data Scientist Interview Questions and Answers

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: r...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

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 combi...read more

I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.

• Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.

• Preprocessed and cleaned the data to handle missing values and outliers.

• Built and trained a machine learning model using algorithms like logistic regression and random forest.

• Evaluated the model's perform...read more

Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.

• One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.

• Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.

• Evaluate the model using appropriate...read more

Faster-RCNN and Yolo v3 are both object detection algorithms, but differ in their approach and performance.

• Faster-RCNN uses a two-stage approach, first generating region proposals and then classifying them.

• Yolo v3 uses a single-stage approach, directly predicting bounding boxes and class probabilities.

• Faster-RCNN is generally more accurate but slower, while Yolo v3 is faster but less accurate.

• Faster-RCNN is better suit...read more

RNN uses techniques like gradient clipping, weight initialization, and LSTM/GRU cells to handle exploding/vanishing gradients.

• Gradient clipping limits the magnitude of gradients during backpropagation.

• Weight initialization techniques like Xavier initialization help in preventing vanishing gradients.

• LSTM/GRU cells have gating mechanisms that allow the network to selectively remember or forget information.

• Batch normaliza...read more

ACF measures the linear relationship between an observation and its lagged values. PACF measures the direct relationship.

• ACF (Autocorrelation Function) measures the correlation between an observation and its lagged values.

• PACF (Partial Autocorrelation Function) measures the correlation between an observation and its lagged values, while removing the indirect effects of intermediate lags.

• ACF is used to identify the orde...read more