Feynn Labs Interview Questions, Process, and Tips

Q1. What is svm,how many dimensions in rbf?
Ans. 
SVM stands for Support Vector Machine, RBF stands for Radial Basis Function. RBF can have infinite dimensions.
• SVM is a supervised machine learning algorithm used for classification and regression tasks.

• RBF is a kernel function used in SVM to map data into a higher-dimensional space.

• RBF can have infinite dimensions, allowing it to capture complex relationships in the data.
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Q2. Different between logistic and linear regression
Ans. 
Logistic regression is used for binary classification while linear regression is used for regression tasks.
• Logistic regression predicts the probability of a binary outcome (0 or 1), while linear regression predicts a continuous outcome.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regression uses a linear function.

• Logistic regression is more suitable for classificatio...read more
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Q3. What is random partition
Ans. 
Random partition is a method of dividing a dataset into random subsets for training and testing purposes.
• Random partition helps in evaluating the performance of a machine learning model by training it on one subset and testing it on another.

• It helps in preventing overfitting by ensuring that the model is tested on unseen data.

• Random partition is commonly used in techniques like k-fold cross-validation where the dataset
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Q1. What is logistic regression
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (e.g., 0 or 1, yes or no).

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function (sig...read more
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Q2. How can you use K - Means?
Ans. 
K-Means is a clustering algorithm used to group data points into K clusters based on similarity.
• Choose the number of clusters (K) you want to create

• Randomly initialize K cluster centroids

• Assign each data point to the nearest centroid

• Update the centroids based on the mean of the data points assigned to each cluster

• Repeat the assignment and update steps until convergence
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Q1. What types of machine learning project you worked on?
Ans. 
I have worked on projects involving image classification, natural language processing, and predictive modeling.
• Image classification using convolutional neural networks

• Sentiment analysis using recurrent neural networks

• Predictive modeling for sales forecasting
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Q2. What is difference between logistics and linear regression?
Ans. 
Logistic regression is used for binary classification while linear regression is used for regression tasks.
• Logistic regression is used when the dependent variable is binary (0 or 1), while linear regression is used when the dependent variable is continuous.

• Logistic regression predicts the probability of a certain class or event occurring, while linear regression predicts a continuous value.

• Logistic regression uses a si...read more
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Q1. What is random forest? What it is called random?
Ans. 
Random forest is an ensemble learning method used for classification and regression tasks, consisting of multiple decision trees.
• Random forest is made up of multiple decision trees, where each tree is built using a subset of the training data and a random subset of features.

• During prediction, each tree in the forest independently predicts the output, and the final output is determined by a majority vote (classification...read more
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Q2. What is svm? Any project you perform using this?
Ans. 
SVM stands for Support Vector Machine, a supervised machine learning algorithm used for classification and regression tasks.
• SVM finds the hyperplane that best separates different classes in the feature space.

• It can handle both linear and non-linear data by using different kernel functions.

• Example project: Sentiment analysis using SVM to classify movie reviews as positive or negative.
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