On 2020-05-08 21:19, joseph pareti wrote:
yet, something is still unclear; in Python you can do things like:
*clf0.fit(X_train,y_train)*
which is not the way I programmed in other languages where a left-hand
side and a right hand side is required.
All it's doing is performing the calculation and then storing the result
in the object itself. Most other languages can do that too.
Am Fr., 8. Mai 2020 um 21:52 Uhr schrieb joseph pareti
<joeparet...@gmail.com <mailto:joeparet...@gmail.com>>:
yes, it is random forest classifier from scikit learn. Thank you.
Am Fr., 8. Mai 2020 um 21:50 Uhr schrieb MRAB
<pyt...@mrabarnett.plus.com <mailto:pyt...@mrabarnett.plus.com>>:
On 2020-05-08 20:02, joseph pareti wrote:
> In general I prefer doing:
>
>
> X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.33, random_state=42)
>clf = RandomForestClassifier(n_estimators = 100, max_depth=
> None) *clf_f = clf.fit(X_train, y_train)* predicted_labels =
clf_f.predict(
> X_test) score = clf.score(X_test, y_test) score1 =
metrics.accuracy_score(
> y_test, predicted_labels)
>
>
> rather than:
>
> X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.33,
> random_state=42)
clf0=RandomForestClassifier(n_estimators=100, max_depth=
> None) *clf0.fit(X_train, y_train)* y_pred
=clf0.predict(X_test) score=
> metrics.accuracy_score(y_test, y_pred)
>
>
> Are the two codes really equivalent?
>
You didn't give any context and say what package you're using!
After searching for "RandomForestClassifier", I'm guessing
that you're
using scikit.
From the documentation here:
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit
it says:
Returns: self : object
so it looks like clf.fit(...) returns clf.
That being the case, then, yes, they're equivalent.
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