--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification model_format: pickle model_file: model.pkl --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |-------------------------------------|-------------------------------------------| | memory | | | steps | [('tfidf', TfidfVectorizer(min_df=100, ngram_range=(1, 3),
preprocessor=)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...))] | | verbose | True | | tfidf | TfidfVectorizer(min_df=100, ngram_range=(1, 3),
preprocessor=) | | classifier | XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...) | | tfidf__analyzer | word | | tfidf__binary | False | | tfidf__decode_error | strict | | tfidf__dtype | | | tfidf__encoding | utf-8 | | tfidf__input | content | | tfidf__lowercase | True | | tfidf__max_df | 1.0 | | tfidf__max_features | | | tfidf__min_df | 100 | | tfidf__ngram_range | (1, 3) | | tfidf__norm | l2 | | tfidf__preprocessor | | | tfidf__smooth_idf | True | | tfidf__stop_words | | | tfidf__strip_accents | | | tfidf__sublinear_tf | False | | tfidf__token_pattern | (?u)\b\w\w+\b | | tfidf__tokenizer | | | tfidf__use_idf | True | | tfidf__vocabulary | | | classifier__objective | binary:logistic | | classifier__base_score | | | classifier__booster | | | classifier__callbacks | | | classifier__colsample_bylevel | | | classifier__colsample_bynode | | | classifier__colsample_bytree | | | classifier__device | | | classifier__early_stopping_rounds | | | classifier__enable_categorical | False | | classifier__eval_metric | | | classifier__feature_types | | | classifier__gamma | | | classifier__grow_policy | | | classifier__importance_type | | | classifier__interaction_constraints | | | classifier__learning_rate | | | classifier__max_bin | | | classifier__max_cat_threshold | | | classifier__max_cat_to_onehot | | | classifier__max_delta_step | | | classifier__max_depth | | | classifier__max_leaves | | | classifier__min_child_weight | | | classifier__missing | nan | | classifier__monotone_constraints | | | classifier__multi_strategy | | | classifier__n_estimators | | | classifier__n_jobs | | | classifier__num_parallel_tree | | | classifier__random_state | | | classifier__reg_alpha | | | classifier__reg_lambda | | | classifier__sampling_method | | | classifier__scale_pos_weight | | | classifier__subsample | | | classifier__tree_method | | | classifier__validate_parameters | | | classifier__verbosity | |
### Model Plot 
Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7fa438e7a280>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)
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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.910317 | | f1 score | 0.910317 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # get_started_code import sklearn import dill as pickle from skops import hub_utils from pathlib import Path suicide_detector_repo = Path("./suicide-detector") hub_utils.download( repo_id="AndyJamesTurner/suicideDetector", dst=suicide_detector_repo ) with open(suicide_detector_repo / "model.pkl", 'rb') as file: clf = pickle.load(file) classification = clf.predict(["I want to kill myself"])[0] # model_card_authors Andy Turner # model_description Suicide Detection text classification model. Trained on the Suicide and Depression Detection dataset (https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch) The model vectorises each text using a trained tfidf vectorizer and then classifies using xgboost. # eval_method The model was evaluated on a 0.3 holdout split using f1 score, accuracy, confusion matrix and ROC curves. # Confusion matrix ![Confusion matrix](confusion_matrix.png) # ROC Curve ![ROC Curve](roc_curve.png) # Classification Report
Click to expand | index | precision | recall | f1-score | support | |--------------|-------------|----------|------------|-----------| | not suicide | 0.891721 | 0.934126 | 0.912431 | 34824 | | suicide | 0.930785 | 0.886491 | 0.908098 | 34799 | | macro avg | 0.911253 | 0.910308 | 0.910265 | 69623 | | weighted avg | 0.911246 | 0.910317 | 0.910265 | 69623 |