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I was interviewed in Dec 2022.
I applied via Approached by Company and was interviewed before Apr 2023. There was 1 interview round.
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
Identifying and solving problems related to points, lines, angles, and shapes
Utilizing geometric formulas and theorems to find solutions
Examples include calculating area, perimeter, angles, and distances in geometric figures
I would train a decision tree model as it can handle categorical data well with minimal data.
Decision tree models are suitable for categorical prediction with minimal data
They can handle both numerical and categorical data
Decision trees are easy to interpret and visualize
Examples: predicting customer churn, classifying spam emails
Decision tree algorithm is a tree-like model used for classification and regression. Cross entropy is a measure of the difference between two probability distributions.
Decision tree algorithm recursively splits the data into subsets based on the most significant attribute until a stopping criterion is met.
It is a popular algorithm for both classification and regression tasks.
Cross entropy is used as a loss function in ...
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I applied via LinkedIn and was interviewed before Mar 2023. There was 1 interview round.
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I applied via Campus Placement and was interviewed before Jun 2023. There was 1 interview round.
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I applied via Walk-in and was interviewed before Mar 2023. There was 1 interview round.
Bias-variance trade off is the balance between underfitting and overfitting in machine learning models.
Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.
Variance refers to error from sensitivity to small fluctuations in the training set, leading to overfitting.
The trade off involves finding the right level of model complexity to minimize both bias and variance.
Regulariza...
I applied via Referral and was interviewed in May 2022. There was 1 interview round.
I was interviewed before Mar 2023.
K-means is a clustering algorithm while KNN is a classification algorithm.
K-means is unsupervised learning, KNN is supervised learning
K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors
K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)
Example: K-means can be used to group customers based on purc...
I applied via Referral and was interviewed in May 2022. There were 3 interview rounds.
There was details syllabus and couse cover
Around 20 people participating in o e gd
The duration of TCS Data Scientist interview process can vary, but typically it takes about less than 2 weeks to complete.
2 Interview rounds
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