Top 250 Machine Learning Interview Questions and Answers

Random Forest is an ensemble learning algorithm that builds multiple decision trees and combines their outputs.

• Random Forest is a supervised learning algorithm.

• It can be used for both classification and regression tasks.

• It creates multiple decision t...read more

Variables importance in predictive model is determined using techniques like feature selection, correlation analysis, and machine learning algorithms.

• Use feature selection techniques like Recursive Feature Elimination (RFE) or SelectKBest to identify...read more

Embeddings are a way to represent words or phrases as vectors in a high-dimensional space.

• Embeddings are learned through neural networks that analyze large amounts of text data.

• They capture semantic and syntactic relationships between words.

• They are ...read more

AUC (Area Under the Curve) is a metric that measures the performance of a classification model. ROC (Receiver Operating Characteristic) is a graphical representation of the AUC.

• AUC is a single scalar value that represents the area under the ROC curve...read more

Transformer architecture is a neural network architecture used for natural language processing tasks. Positional encoders are used to encode the position of words in a sentence.

• Transformer architecture is based on the self-attention mechanism.

• It cons...read more

Clustering algorithms are unsupervised machine learning techniques used to group similar data points together.

• Clustering algorithms are used to identify patterns in data by grouping similar data points together.

• They are unsupervised machine learning ...read more

A transformer is an electrical device that transfers electrical energy between two or more circuits through electromagnetic induction.

• Transformers are commonly used in power distribution systems to step up or step down voltage levels.

• They consist of ...read more

CNN models are deep neural networks used for image classification and object recognition.

• CNN models use convolutional layers to extract features from images

• Pooling layers are used to reduce the spatial dimensions of the feature maps

• Fully connected la...read more

ROC and AUC are performance metrics used in binary classification models.

• ROC (Receiver Operating Characteristic) is a curve that plots the true positive rate against the false positive rate at different classification thresholds.

• AUC (Area Under the C...read more

Event Detection is the process of identifying and extracting meaningful events from data streams.

• It involves analyzing data in real-time to detect patterns and anomalies

• It is commonly used in fields such as finance, social media, and security

• Examples...read more

There are three types of machine learning: supervised, unsupervised, and reinforcement learning.

• Supervised learning involves training a model on labeled data to make predictions on new data. Example: predicting house prices based on features like loc...read more

A lathe machine is a tool that rotates a workpiece on its axis to perform various operations such as cutting, drilling, and sanding.

• Lathe machines are commonly used in metalworking and woodworking industries.

• They consist of a rotating spindle, a chuc...read more

MLOps is a practice that aims to streamline the machine learning lifecycle from development to deployment and monitoring.

• MLOps combines machine learning (ML) and DevOps practices to improve the efficiency and effectiveness of ML models.

• It involves au...read more

To choose optimum probability threshold from ROC, we need to balance between sensitivity and specificity.

• Choose the threshold that maximizes the sum of sensitivity and specificity

• Use Youden's J statistic to find the optimal threshold

• Consider the cost...read more

Developed a machine learning model to predict customer churn for a telecom company.

• Collected and cleaned customer data including usage patterns and demographics

• Used algorithms like logistic regression and random forest to train the model

• Evaluated mod...read more

Validation sampling is a process of selecting a subset of data from a larger population to assess the accuracy and reliability of a validation method.

• Validation sampling is used to evaluate the performance of a validation process or method.

• It involve...read more

Balancing precision and recall involves trade-offs to optimize model performance based on specific use cases.

• Understand the context: In medical diagnosis, high recall is crucial to avoid missing diseases.

• Adjust thresholds: Lowering the decision thres...read more

Bias in ML refers to the systematic error in a model's predictions, leading to inaccurate results.

• Bias is the algorithm's tendency to consistently learn the wrong thing by not taking all factors into account.

• It can result from the data used to train ...read more

Multicollinearity in regression analysis causes issues like inflated standard errors, unstable coefficients, and difficulty in interpreting the importance of predictors.

• Multicollinearity leads to inflated standard errors, making it difficult to deter...read more

Variable selection can be done using techniques like correlation matrix, stepwise regression, and principal component analysis.

• Check for correlation between variables using correlation matrix

• Use stepwise regression to select variables based on their ...read more

Cross entropy loss is used in classification because it penalizes incorrect classifications more heavily, making it more suitable for classification tasks compared to SSE.

• Cross entropy loss is more suitable for classification tasks because it penaliz...read more

The machine learning model used on our website is a recommendation system based on collaborative filtering.

• The website uses collaborative filtering to recommend products or content to users based on their past interactions and similarities with other...read more

Random Forest is an ensemble learning method that builds multiple decision trees and combines their predictions, while XGBoost is a gradient boosting algorithm that builds trees sequentially.

• Random Forest builds multiple decision trees independently ...read more

Prediction uses data analysis and statistical models to forecast future outcomes.

• Prediction involves collecting and analyzing data to identify patterns and trends.

• Statistical models are then used to make predictions based on the identified patterns.

• P...read more

Classification metrics are used to evaluate the performance of a classification model by measuring its accuracy, precision, recall, F1 score, and more.

• Classification metrics help in assessing how well a model is performing in terms of predicting the ...read more

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, preci...read more

K-means is a clustering algorithm that partitions data into k clusters based on similarity.

• K-means is an unsupervised learning algorithm

• It starts by randomly selecting k centroids

• Data points are assigned to the nearest centroid

• Centroids are recalcula...read more