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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 Approached by Company and was interviewed in Nov 2024. There were 3 interview rounds.
I applied via Recruitment Consulltant and was interviewed in Oct 2024. There was 1 interview round.
I applied via Company Website and was interviewed in Jul 2024. There were 5 interview rounds.
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.
Adam optimizer uses adaptive learning rates for each parameter.
Gradient Descent optimizer has a fixed learning rate for all parameters.
Adam optimizer includes momentum to speed up convergence.
Gradient Descent optimizer updates parameters b...
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.
Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.
Consider using Leaky ReLU or Sigmoid for output layers depending on the task.
ReLU is computationally efficient and helps in preventing the vanishing gradient prob...
I applied via Approached by Company and was interviewed in Apr 2024. There was 1 interview round.
I applied via Company Website and was interviewed in Sep 2023. There were 4 interview rounds.
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...
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