10+ Jungleworks Interview Questions and Answers

Mission learning is used for data analysis and prediction with additional algorithms for AI.

• Mission learning is a subset of machine learning that focuses on predicting outcomes based on data analysis.

• It involves using algorithms to learn patterns and make predictions based on new data.

• Examples include image recognition, natural language processing, and recommendation systems.

The choice of ML model depends on the problem, data, and desired outcome.

• Consider the problem type: classification, regression, clustering, etc.

• Analyze the data: size, quality, features, and target variable.

• Evaluate model performance: accuracy, precision, recall, F1-score.

• Consider interpretability, scalability, and computational requirements.

• Experiment with multiple models: decision trees, SVM, neural networks, etc.

• Use cross-validation and hyperparameter tuning for model sele...read more

Different splitting criteria in decision trees include Gini impurity, entropy, and misclassification error. Random forest works better due to ensemble learning and reducing overfitting.

• Splitting criteria in decision trees: Gini impurity, entropy, misclassification error

• Random forest works better due to ensemble learning and reducing overfitting

• Random forest combines multiple decision trees to improve accuracy and generalization

• Random forest introduces randomness in feature se...read more

The question asks about the last 2 projects, ML algorithms, the difference between random forest and xgboost, hyperparameter tuning, NLP questions, and chi-sq test.

• Discuss the details of the last 2 projects you worked on

• Explain various machine learning algorithms you are familiar with

• Highlight the differences between random forest and xgboost

• Describe your experience with hyperparameter tuning

• Answer NLP-related questions

• Explain the chi-sq test and its applications

To generate embeddings on a data set, preprocess the data, choose a suitable embedding method, train the model, and extract the embeddings.

• Preprocess the data by cleaning, tokenizing, and normalizing text data.

• Choose a suitable embedding method such as Word2Vec, GloVe, or FastText.

• Train the embedding model on the preprocessed data to learn the embeddings.

• Extract the embeddings from the trained model to represent the data in a lower-dimensional space.

• Use the embeddings for dow...read more

I am proficient in Python, R, and SQL for data modeling and analysis.

• Python

• R

• SQL

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previous ones. Examples inc...read more

Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.

• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization to new, unseen data.

• Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

• Example: A decision tree with too many branches that perfectly fits the training data but performs poorly on test data.

Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.

• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')

CNN is used for image recognition, RNN is used for sequential data like text or time series.

• CNN is Convolutional Neural Network, used for image recognition tasks.

• RNN is Recurrent Neural Network, used for sequential data like text or time series.

• CNN uses convolutional layers to extract features from images, while RNN uses recurrent connections to remember past information.

• CNN is good at capturing spatial dependencies in data, while RNN is good at capturing temporal dependencie...read more