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Implement predict_proba for RandomForestClassifier #288

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Implement predict_proba for RandomForestClassifier #288

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68fd27f
Implement predict_proba for DecisionTreeClassifier
Mec-iS Jan 20, 2025
58ee0cb
Some automated fixes suggested by cargo clippy --fix
Mec-iS Jan 20, 2025
609f802
more clippy fixes
Mec-iS Jan 20, 2025
fc7f2e6
format
Mec-iS Jan 20, 2025
5711788
add proper error handling
Mec-iS Jan 20, 2025
40ee35b
Implement predict_proba for RandomForestClassifier
Mec-iS Jan 20, 2025
63fa003
Fix clippy error
Mec-iS Jan 20, 2025
52b797d
format
Mec-iS Jan 20, 2025
bb356e6
fix test
Mec-iS Jan 20, 2025
5d6ed49
Merge branch 'development' into issue-50-predict-proba-for-randomforest
Mec-iS Jan 20, 2025
0262dae
Merge branch 'development' of github.com:smartcorelib/smartcore into …
Mec-iS Jan 20, 2025
d427c91
try to fix test error
Mec-iS Jan 20, 2025
4878042
Merge branch 'issue-50-predict-proba-for-randomforest' of github.com:…
Mec-iS Jan 20, 2025
4aee603
fix test conditions
Mec-iS Jan 22, 2025
7878078
Update ci.yml
Mec-iS Jan 22, 2025
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fix test
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@Mec-iS
Mec-iS committed Jan 20, 2025
commit bb356e6a289209ad586eaf7af20217c12125a2ae
12 changes: 7 additions & 5 deletions src/ensemble/random_forest_classifier.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -833,8 +833,9 @@ mod tests {
)]
#[test]
fn test_random_forest_predict_proba() {
use num_traits::FromPrimitive;
// Iris-like dataset (subset)
let x = DenseMatrix::from_2d_array(&[
let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
&[4.7, 3.2, 1.3, 0.2],
Expand Down Expand Up @@ -881,21 +882,22 @@ mod tests {
// These values are approximate and based on typical random forest behavior
for i in 0..5 {
assert!(
*probas.get((i, 0)) > 0.6,
*probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
"Class 0 samples should have high probability for class 0"
);
assert!(
*probas.get((i, 1)) < 0.4,
*probas.get((i, 1)) < f64::from_f32(0.4).unwrap(),
"Class 0 samples should have low probability for class 1"
);
}

for i in 5..10 {
assert!(
*probas.get((i, 1)) > 0.6,
*probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
"Class 1 samples should have high probability for class 1"
);
assert!(
*probas.get((i, 0)) < 0.4,
*probas.get((i, 0)) < f64::from_f32(0.4).unwrap(),
"Class 1 samples should have low probability for class 0"
);
}
Expand Down
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