kantundpeterpan/frugal-ai-toy

Model description

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Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('lemmatizer', FunctionTransformer(func=<function lemmatize_X at 0x7fb4a5fb33a0>)), ('tfidf', TfidfVectorizer(max_df=0.95, min_df=2, stop_words=['just', 'himself', 'most', '"', '
transform_input
verbose	False
lemmatizer	FunctionTransformer(func=<function lemmatize_X at 0x7fb4a5fb33a0>)
tfidf	TfidfVectorizer(max_df=0.95, min_df=2, stop_words=['just', 'himself', 'most', '"', '
rf	RandomForestClassifier()
lemmatizer__accept_sparse	False
lemmatizer__check_inverse	True
lemmatizer__feature_names_out
lemmatizer__func	<function lemmatize_X at 0x7fb4a5fb33a0>
lemmatizer__inv_kw_args
lemmatizer__inverse_func
lemmatizer__kw_args
lemmatizer__validate	False
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	0.95
tfidf__max_features
tfidf__min_df	2
tfidf__ngram_range	(1, 1)
tfidf__norm	l2
tfidf__preprocessor
tfidf__smooth_idf	True
tfidf__stop_words	['just', 'himself', 'most', '"', '
tfidf__strip_accents
tfidf__sublinear_tf	False
tfidf__token_pattern	(?u)\b\w\w+\b
tfidf__tokenizer	<function tokenize_quote at 0x7fb4a5fb31f0>
tfidf__use_idf	True
tfidf__vocabulary
rf__bootstrap	True
rf__ccp_alpha	0.0
rf__class_weight
rf__criterion	gini
rf__max_depth
rf__max_features	sqrt
rf__max_leaf_nodes
rf__max_samples
rf__min_impurity_decrease	0.0
rf__min_samples_leaf	1
rf__min_samples_split	2
rf__min_weight_fraction_leaf	0.0
rf__monotonic_cst
rf__n_estimators	100
rf__n_jobs
rf__oob_score	False
rf__random_state
rf__verbose	0
rf__warm_start	False

Model Plot

Pipeline(steps=[('lemmatizer',FunctionTransformer(func=<function lemmatize_X at 0x7fb4a5fb33a0>)),('tfidf',TfidfVectorizer(max_df=0.95, min_df=2,stop_words=['just', 'himself', 'most', '"','|', 'where', 'didn', 'with', '(','at', 'once', 'she', 'do', 'below','y', 'having', 'about', '^', ')','@', 'theirs', "wasn't", 'the','wouldn', 'for', 'after', 'to',"you'll", ';', 'over', ...],tokenizer=<function tokenize_quote at 0x7fb4a5fb31f0>)),('rf', RandomForestClassifier())])

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Evaluation Results

Metric	Value
accuracy	0.867925
f1 score	0.867925

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Confusion Matrix