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Q. 481) scikit-learn also provides functions for creating dummy datasets from scratch:
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Q. 482) which can accept a NumPy RandomState generator or an integer seed.
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Q. 483) In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least valid options
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Q. 484) is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
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Q. 485) It's possible to specify if the scaling process must include both mean and standard deviation using the parameters .
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Q. 486) Which of the following selects the best K high-score features.
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Q. 487) What is/are true about ridge regression?1. When lambda is 0, model works like linear regression model2. When lambda is 0, model doesn’t work like linear regression model3. When lambda goes to infinity, we get very, very small coefficients approaching 04. When lambda goes to infinity, we get very, very large coefficients approaching infinity
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Q. 488) Function used for linear regression in R is
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Q. 489) In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to
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Q. 490) Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.Which of the following is true for X1?
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