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I applied via Naukri.com and was interviewed in Sep 2024. There was 1 interview round.
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.
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...
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
I applied via Naukri.com and was interviewed in Aug 2024. There was 1 interview round.
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...
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
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
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
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...
I was interviewed in Jul 2024.
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...
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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Random partition involves splitting data randomly, while ordering partition involves splitting data based on a specific order.
Random partition randomly divides data into subsets without any specific order.
Ordering partition divides data into subsets based on a specific order, such as time or alphabetical order.
Random partition is useful for creating training and testing sets for machine learning models.
Ordering partiti...
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) based on one or more independent variables.
Linear regression predicts a continuous outcome based on one or more independent variables.
Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regressio...
KNN is a supervised learning algorithm used for classification and regression, while K Means is an unsupervised clustering algorithm.
KNN stands for K-Nearest Neighbors and assigns a class label based on majority voting of its k-nearest neighbors.
K Means is a clustering algorithm that partitions data into k clusters based on similarity.
KNN requires labeled data for training, while K Means does not need labeled data.
KNN ...
Cross entropy loss measures the difference between two probability distributions.
Range of cross entropy loss is [0, infinity)
Lower values indicate better model performance
Commonly used in classification tasks
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I applied via Naukri.com and was interviewed in Jul 2024. There were 2 interview rounds.
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.
Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.
In linear regression, the relationship between the independent and dependent variables...
Data Science Project
posted on 14 Jul 2024
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) based on input features.
Linear regression predicts a continuous value based on input features.
Logistic regression uses a sigmoid function to map predicted values between 0 and 1.
Linear regression uses a linear equation to model the relations...
Loss functions are used to measure the difference between predicted values and actual values in machine learning models.
Loss functions quantify how well a model is performing by comparing predicted values to actual values
Common loss functions include Mean Squared Error (MSE), Cross Entropy Loss, and Hinge Loss
Different loss functions are used for different types of machine learning tasks, such as regression or classifi
Write the code for logistic Regression
I applied via Company Website and was interviewed in May 2024. There was 1 interview round.
I applied via Naukri.com and was interviewed in May 2024. There was 1 interview round.
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