🔒 Data Bases, Data Science,

[iron mountain]

Define positive_revenue_df as the subset of movies in df with revenue greater than zero.
Code is provided below that creates new instances of model objects. Replace all instances of df with positive_revenue_df, and run the given code.
Use this code to get started:







      positive_revenue_df = 

# Replace the dataframe in the following code, and run.
regression_outcome = df[regression_target]
classification_outcome = df[classification_target]
covariates = df[all_covariates]

# Reinstantiate all regression models and classifiers.
linear_regression = LinearRegression()
logistic_regression = LogisticRegression()
forest_regression = RandomForestRegressor(max_depth=4, random_state=0)
forest_classifier = RandomForestClassifier(max_depth=4, random_state=0)
linear_regression_scores = cross_val_score(linear_regression, covariates, regression_outcome, cv=10, scoring=correlation)
forest_regression_scores = cross_val_score(forest_regression, covariates, regression_outcome, cv=10, scoring=correlation)
logistic_regression_scores = cross_val_score(logistic_regression, covariates, classification_outcome, cv=10, scoring=accuracy)
forest_classification_scores = cross_val_score(forest_classifier, covariates, classification_outcome, cv=10, scoring=accuracy)
    
What is the mean of the 10 cross validation scores for random forest regression?
 
It appears that the variables budget, popularity, runtime, vote_count, and revenue are all right-skewed. In Exercise 6, we will transform these variables to eliminate this skewness. Specifically, we will use the np.log10() method. Because some of these variable values are exactly 0, we will add a small positive value to each to ensure it is defined; this is necessary because log(0) is negative infinity.

Instructions
For each above-mentioned variable in df, transform value x into np.log10(1+x).

What is the new value of skew() for the covariate runtime? Please provide the answer to 3 decimal points

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