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I applied via LinkedIn and was interviewed in Mar 2024. There was 1 interview round.
Using R as a calculator to compute values for a Data Scientist interview question.
Use R's console to input mathematical expressions and compute values.
Make sure to follow the order of operations (PEMDAS) when entering expressions.
Use functions like 'sqrt()' for square roots and 'exp()' for exponentiation.
Remember to assign variables using the '<-' operator before using them in calculations.
Compute statistics of a given time value in R
Use lubridate package to work with time data in R
Calculate summary statistics like mean, median, min, max, and standard deviation
Convert the time value to a time object before performing calculations
Using R to create two matrices and perform matrix multiplication.
Create two matrices using matrix() function in R.
Use %*% operator for matrix multiplication.
Ensure the dimensions of the matrices are compatible for multiplication.
The kNN classifier is run on the iris data to make predictions based on nearest neighbors.
kNN classifier is a type of supervised machine learning algorithm that can be used for classification tasks.
The output will be the predicted class labels for the iris data based on the nearest neighbors.
Interpreting the output involves understanding how the algorithm has classified the data points.
Top trending discussions
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
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