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I applied via Company Website and was interviewed in Jul 2024. There were 5 interview rounds.
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I applied via Company Website and was interviewed in Dec 2024. There were 3 interview rounds.
Basic self evaluation test.
Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.
Use resampling techniques like oversampling or undersampling to balance the classes.
Utilize algorithms that are robust to class imbalance, such as Random Forest, XGBoost, or SVM.
Adjust class weights in the model to give more importance to minority class.
Use evaluation metrics like F1 score, precision, r...
I applied via Recruitment Consulltant and was interviewed in Oct 2024. There was 1 interview round.
I applied via Campus Placement and was interviewed in Nov 2023. There was 1 interview round.
I applied via Approached by Company and was interviewed in Aug 2023. There was 1 interview round.
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...
posted on 21 Oct 2022
I applied via Approached by Company and was interviewed in Sep 2022. There were 3 interview rounds.
I applied via LinkedIn and was interviewed in Aug 2024. There was 1 interview round.
Developed a machine learning model to predict customer churn for a telecom company
Used Python and scikit-learn for data preprocessing and model building
Performed feature engineering to improve model performance
Evaluated model using metrics like accuracy, precision, and recall
Implemented the model in a production environment for real-time predictions
posted on 1 Jul 2024
Decision Trees are a popular machine learning algorithm used for classification and regression tasks.
Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.
They are easy to interpret and visualize, making them popular for exploratory data analysis.
Decision Trees can handle both numerical and c...
LLMs can be finetuned by adjusting hyperparameters, training on specific datasets, and using techniques like transfer learning.
Adjust hyperparameters such as learning rate, batch size, and number of layers to improve performance.
Train the LLM on domain-specific datasets to improve its understanding of specialized language.
Utilize transfer learning by starting with a pre-trained LLM model and fine-tuning it on a smaller...
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