README.md

metadata

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=<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, 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=<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, 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	<class 'numpy.float64'>
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	<function preprocessor at 0x7fa438e7a280>
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:

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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

ROC Curve

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