metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: skops
model_file: skops-4mj4y_67.skops
widget:
  - structuredData:
      amplitude_cutoff:
        - .nan
        - .nan
        - .nan
      amplitude_cv_median:
        - .nan
        - .nan
        - .nan
      amplitude_cv_range:
        - .nan
        - .nan
        - .nan
      amplitude_median:
        - -231.14950561523438
        - -32.41670227050781
        - -49.5401496887207
      drift_mad:
        - .nan
        - .nan
        - .nan
      drift_ptp:
        - .nan
        - .nan
        - .nan
      drift_std:
        - .nan
        - .nan
        - .nan
      firing_range:
        - 1.8000000000000007
        - 3.2399999999999984
        - 1.4399999999999995
      firing_rate:
        - 14.4
        - 14.6
        - 13.8
      isi_violations_count:
        - 0
        - 0
        - 0
      isi_violations_ratio:
        - 0
        - 0
        - 0
      num_spikes:
        - 144
        - 146
        - 138
      presence_ratio:
        - .nan
        - .nan
        - .nan
      rp_contamination:
        - 0
        - 0
        - 0
      rp_violations:
        - 0
        - 0
        - 0
      sd_ratio:
        - 0.5912728859813103
        - 1.1242492492431155
        - 0.7087562828230378
      sliding_rp_violation:
        - 0.14
        - 0.13
        - 0.145
      snr:
        - 40.52572890814601
        - 6.3489456520122625
        - 9.014227884573495
      sync_spike_2:
        - 0
        - 0
        - 0.007246376811594203
      sync_spike_4:
        - 0
        - 0
        - 0
      sync_spike_8:
        - 0
        - 0
        - 0

Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
steps	[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('classifier', RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3, min_samples_split=3, n_estimators=103, random_state=404159593))]
verbose	False
imputer	SimpleImputer(strategy='median')
scaler	StandardScaler()
classifier	RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3, min_samples_split=3, n_estimators=103, random_state=404159593)
imputer__add_indicator	False
imputer__copy	True
imputer__fill_value
imputer__keep_empty_features	False
imputer__missing_values	nan
imputer__strategy	median
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
classifier__bootstrap	True
classifier__ccp_alpha	0.0
classifier__class_weight	balanced_subsample
classifier__criterion	gini
classifier__max_depth
classifier__max_features	sqrt
classifier__max_leaf_nodes
classifier__max_samples
classifier__min_impurity_decrease	0.0
classifier__min_samples_leaf	3
classifier__min_samples_split	3
classifier__min_weight_fraction_leaf	0.0
classifier__monotonic_cst
classifier__n_estimators	103
classifier__n_jobs
classifier__oob_score	False
classifier__random_state	404159593
classifier__verbose	0
classifier__warm_start	False

Model Plot

Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),('scaler', StandardScaler()),('classifier',RandomForestClassifier(class_weight='balanced_subsample',min_samples_leaf=3, min_samples_split=3,n_estimators=103,random_state=404159593))])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

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Citation

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BibTeX:

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