# scale by factor of 1 or 2
Code
not_recommended_standardized_input <- rec %>% step_scale(carbon, id = "scale",
factor = 3) %>% prep(training = biomass)
Condition
Warning:
Scaling `factor` should take either a value of 1 or 2
# printing
Code
print(standardized)
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 5
Operations:
Centering for carbon
Scaling for hydrogen
Centering and scaling for nitrogen, carbon
---
Code
prep(standardized)
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 5
Training data contained 536 data points and no missing data.
Operations:
Centering for carbon [trained]
Scaling for hydrogen [trained]
Centering and scaling for nitrogen, carbon [trained]
# center - empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Centering for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Centering for <none> [trained]
# scale - empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Scaling for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Scaling for <none> [trained]
# scale - warns on zv
Code
prep(rec1)
Condition
Warning:
Column(s) have zero variance so scaling cannot be used: `zero_variance`. Consider using `step_zv()` to remove those columns before normalizing
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 6
Training data contained 536 data points and no missing data.
Operations:
Scaling for carbon, hydrogen, oxygen, nitrogen, sulfur, zer... [trained]
# normalize - empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Centering and scaling for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Centering and scaling for <none> [trained]
# normalize - warns on zv
Code
prep(rec1)
Condition
Warning:
Column(s) have zero variance so scaling cannot be used: `zero_variance`. Consider using `step_zv()` to remove those columns before normalizing
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 6
Training data contained 536 data points and no missing data.
Operations:
Centering and scaling for carbon, hydrogen, oxygen, nitrogen, sulfur, zer... [trained]
# centering with case weights
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Centering for disp, hp, drat, wt, qsec, vs, am, gear, carb [weighted, trained]
---
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Centering for cyl, disp, hp, drat, qsec, vs, am, gear, carb [ignored weights, trained]
# scaling with case weights
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Scaling for disp, hp, drat, wt, qsec, vs, am, gear, carb [weighted, trained]
---
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Scaling for cyl, disp, hp, drat, qsec, vs, am, gear, carb [ignored weights, trained]
# normalizing with case weights
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Centering and scaling for disp, hp, drat, wt, qsec, vs, am, gear, carb [weighted, trained]
---
Code
rec
Output
Recipe
Inputs:
role #variables
case_weights 1
outcome 1
predictor 9
Training data contained 32 data points and no missing data.
Operations:
Centering and scaling for cyl, disp, hp, drat, qsec, vs, am, gear, carb [ignored weights, trained]
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