13. A data analyst is developing a machine learning model to predict the sales revenue to be generated by each product being sold by a grocery shop. The training dataset has a lot of numeric variable that have different distributions as well las feature ranges. He was advised that he should use numeric transformations to transform the numeric features. Which of the following statements is/are true regarding some numeric transformations? Select all true.Yeo Johnson tranformation result in errors when the numeric feature contains negative valuesYeo Johnson transformation works on both negative and positive numbersBox Cox transformation result in errors when the numeric feature contains negative valuesBoth Cox Cox and Yeo Johnson transformation give error when the numeric values are floatsBoth Box Cox and Yeo Johnson transfomation transform the numeric fetures to normal distribution

A data analyst wants to train a machine learning model to predict the salary of new hires. The training data contains both categorical and numeric features. Which of the following statements is correct regarding categorical and numeric features?Box Cox tranformation and Yeo Johnson transformation can be used on both numeric and categorical featuresBox cox tranformation is used on categorical features while Standard Scaler is used on numerical featuresMin Max Scaler is used on both numeric and categorical columnsYeo Johnson transfomation is used on numeric features while One hot encoder is used on categorical columns

If a feature has a highly skewed distribution, then which of the following transformations should be used to transform the data?Select an option Clear ResponseBinningLog TransformationsGeometric Means TransformationStandard Scaling

1. A Machine Learning Specialist is training a K-NN model that can predict employment rates based on a range of factors such as economic climate, technological advances, seasonal fluctuations, and so on. During data analysis, the Specialist observes different input variables that significantly vary in magnitude. The Specialist wants to avoid having a dataset whose features with a larger value greatly influence the model’s predictive capability. Which transformation method should be taken by the ML-Specialist to achieve his goal?Normalization TransformationN-gram TransformationQuantile Binning TransformationOrthogonal Sparse Bigram (OSB) Transformation

2. You are creating a training pipeline for a regression model. You use a dataset that has multiple numeric columns in which the values are on different scales. You want to transform the numeric columns so that the values are all on a similar scale. You also want the transformation to scale relative to the minimum and maximum values in each column. Which module should you add to the pipeline? Select Columns in a DatasetClean Missing DataNormalize Data

Which statements describe one of the transformations performed on 𝑓(𝑥)=𝑥2f(x)=x 2 to create 𝑔(𝑥)=(𝑥−2)2+5g(x)=(x−2) 2 +5? Choose all that apply.A.A translation of 5 units upB.A translation of 2 units to the leftC.A translation of 2 units to the rightD.A translation of 5 units downSUBMITarrow_backPREVIOUS