0

hi what is the basic difference between 'scoring' and 'metrics'. these are used to measure performance but how do they differ?

if you see the example

in the below the cross val is using 'neg_mean_squared_error' for scoring

X = array[:, 0:13]
Y = array[:, 13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = LinearRegression()
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print("MSE: %.3f (%.3f)") % (results.mean(), results.std())

but in the below xgboost example I am using metrics = 'rmse'

cmatrix = xgb.DMatrix(data=X, label=y)
params = {'objective': 'reg:linear', 'max_depth': 3}
cv_results = xgb.cv(dtrain=cmatrix, params=params, nfold=3, num_boost_round=5, metrics='rmse', as_pandas=True, seed=123)

print(cv_results)
ec2604
  • 501
  • 3
  • 11
TRex
  • 445
  • 5
  • 14

1 Answers1

1

how do they differ?

They don't; these are actually just different terms, to declare the same thing.

To be very precise, scoring is the process in which one measures the model performance, according to some metric (or score). The scikit-learn term choice for the argument scoring (as in your first snippet) is rather unfortunate (it actually implies a scoring function), as the MSE (and its variants, as negative MSE and RMSE) are metrics or scores. But practically speaking, as shown in your example snippets, these two terms are used as synonyms and are frequently used interchangeably.

The real distinction of interest here is not between "score" and "metric", but between loss (often referred to as cost) and metrics such as the accuracy (for classification problems); this is often a source of confusion among new users. You may find my answers in the following threads useful (ignore the Keras mentions in some titles, the answers are generally applicable):

desertnaut
  • 57,590
  • 26
  • 140
  • 166