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---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: RandomForestClassifier.joblib
widget:
- structuredData:
age:
- 50
- 31
- 32
bd2:
- 0.627
- 0.351
- 0.672
id:
- ICU200010
- ICU200011
- ICU200012
insurance:
- 0
- 0
- 1
m11:
- 33.6
- 26.6
- 23.3
pl:
- 148
- 85
- 183
pr:
- 72
- 66
- 64
prg:
- 6
- 1
- 8
sepsis:
- Positive
- Negative
- Positive
sk:
- 35
- 29
- 0
ts:
- 0
- 0
- 0
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
[More Information Needed]
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('preprocessor', ColumnTransformer(transformers=[('numerical_pipeline',<br /> Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]),<br /> ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br /> 'age']),<br /> ('categorical_pipeline',<br /> Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_...<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['insurance']),<br /> ('feature_creation_pipeline',<br /> Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['age'])])), ('feature-selection', SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))] |
| verbose | False |
| preprocessor | ColumnTransformer(transformers=[('numerical_pipeline',<br /> Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]),<br /> ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br /> 'age']),<br /> ('categorical_pipeline',<br /> Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_...<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['insurance']),<br /> ('feature_creation_pipeline',<br /> Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['age'])]) |
| feature-selection | SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x000001E7EDA4E480>) |
| classifier | RandomForestClassifier(n_jobs=-1, random_state=2024) |
| preprocessor__force_int_remainder_cols | True |
| preprocessor__n_jobs | |
| preprocessor__remainder | drop |
| preprocessor__sparse_threshold | 0.3 |
| preprocessor__transformer_weights | |
| preprocessor__transformers | [('numerical_pipeline', Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]), ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']), ('categorical_pipeline', Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]), ['insurance']), ('feature_creation_pipeline', Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]), ['age'])] |
| preprocessor__verbose | False |
| preprocessor__verbose_feature_names_out | True |
| preprocessor__numerical_pipeline | Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]) |
| preprocessor__categorical_pipeline | Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]) |
| preprocessor__feature_creation_pipeline | Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]) |
| preprocessor__numerical_pipeline__memory | |
| preprocessor__numerical_pipeline__steps | [('log_transformations', FunctionTransformer(func=<ufunc 'log1p'>)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] |
| preprocessor__numerical_pipeline__verbose | False |
| preprocessor__numerical_pipeline__log_transformations | FunctionTransformer(func=<ufunc 'log1p'>) |
| preprocessor__numerical_pipeline__imputer | SimpleImputer(strategy='median') |
| preprocessor__numerical_pipeline__scaler | RobustScaler() |
| preprocessor__numerical_pipeline__log_transformations__accept_sparse | False |
| preprocessor__numerical_pipeline__log_transformations__check_inverse | True |
| preprocessor__numerical_pipeline__log_transformations__feature_names_out | |
| preprocessor__numerical_pipeline__log_transformations__func | <ufunc 'log1p'> |
| preprocessor__numerical_pipeline__log_transformations__inv_kw_args | |
| preprocessor__numerical_pipeline__log_transformations__inverse_func | |
| preprocessor__numerical_pipeline__log_transformations__kw_args | |
| preprocessor__numerical_pipeline__log_transformations__validate | False |
| preprocessor__numerical_pipeline__imputer__add_indicator | False |
| preprocessor__numerical_pipeline__imputer__copy | True |
| preprocessor__numerical_pipeline__imputer__fill_value | |
| preprocessor__numerical_pipeline__imputer__keep_empty_features | False |
| preprocessor__numerical_pipeline__imputer__missing_values | nan |
| preprocessor__numerical_pipeline__imputer__strategy | median |
| preprocessor__numerical_pipeline__scaler__copy | True |
| preprocessor__numerical_pipeline__scaler__quantile_range | (25.0, 75.0) |
| preprocessor__numerical_pipeline__scaler__unit_variance | False |
| preprocessor__numerical_pipeline__scaler__with_centering | True |
| preprocessor__numerical_pipeline__scaler__with_scaling | True |
| preprocessor__categorical_pipeline__memory | |
| preprocessor__categorical_pipeline__steps | [('as_categorical', FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False))] |
| preprocessor__categorical_pipeline__verbose | False |
| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=<function as_category at 0x000001E7F1450680>) |
| preprocessor__categorical_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
| preprocessor__categorical_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False) |
| preprocessor__categorical_pipeline__as_categorical__accept_sparse | False |
| preprocessor__categorical_pipeline__as_categorical__check_inverse | True |
| preprocessor__categorical_pipeline__as_categorical__feature_names_out | |
| preprocessor__categorical_pipeline__as_categorical__func | <function as_category at 0x000001E7F1450680> |
| preprocessor__categorical_pipeline__as_categorical__inv_kw_args | |
| preprocessor__categorical_pipeline__as_categorical__inverse_func | |
| preprocessor__categorical_pipeline__as_categorical__kw_args | |
| preprocessor__categorical_pipeline__as_categorical__validate | False |
| preprocessor__categorical_pipeline__imputer__add_indicator | False |
| preprocessor__categorical_pipeline__imputer__copy | True |
| preprocessor__categorical_pipeline__imputer__fill_value | |
| preprocessor__categorical_pipeline__imputer__keep_empty_features | False |
| preprocessor__categorical_pipeline__imputer__missing_values | nan |
| preprocessor__categorical_pipeline__imputer__strategy | most_frequent |
| preprocessor__categorical_pipeline__encoder__categories | auto |
| preprocessor__categorical_pipeline__encoder__drop | first |
| preprocessor__categorical_pipeline__encoder__dtype | <class 'numpy.float64'> |
| preprocessor__categorical_pipeline__encoder__feature_name_combiner | concat |
| preprocessor__categorical_pipeline__encoder__handle_unknown | infrequent_if_exist |
| preprocessor__categorical_pipeline__encoder__max_categories | |
| preprocessor__categorical_pipeline__encoder__min_frequency | |
| preprocessor__categorical_pipeline__encoder__sparse_output | False |
| preprocessor__feature_creation_pipeline__memory | |
| preprocessor__feature_creation_pipeline__steps | [('feature_creation', FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False))] |
| preprocessor__feature_creation_pipeline__verbose | False |
| preprocessor__feature_creation_pipeline__feature_creation | FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>) |
| preprocessor__feature_creation_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
| preprocessor__feature_creation_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False) |
| preprocessor__feature_creation_pipeline__feature_creation__accept_sparse | False |
| preprocessor__feature_creation_pipeline__feature_creation__check_inverse | True |
| preprocessor__feature_creation_pipeline__feature_creation__feature_names_out | |
| preprocessor__feature_creation_pipeline__feature_creation__func | <function feature_creation at 0x000001E7F14514E0> |
| preprocessor__feature_creation_pipeline__feature_creation__inv_kw_args | |
| preprocessor__feature_creation_pipeline__feature_creation__inverse_func | |
| preprocessor__feature_creation_pipeline__feature_creation__kw_args | |
| preprocessor__feature_creation_pipeline__feature_creation__validate | False |
| preprocessor__feature_creation_pipeline__imputer__add_indicator | False |
| preprocessor__feature_creation_pipeline__imputer__copy | True |
| preprocessor__feature_creation_pipeline__imputer__fill_value | |
| preprocessor__feature_creation_pipeline__imputer__keep_empty_features | False |
| preprocessor__feature_creation_pipeline__imputer__missing_values | nan |
| preprocessor__feature_creation_pipeline__imputer__strategy | most_frequent |
| preprocessor__feature_creation_pipeline__encoder__categories | auto |
| preprocessor__feature_creation_pipeline__encoder__drop | first |
| preprocessor__feature_creation_pipeline__encoder__dtype | <class 'numpy.float64'> |
| preprocessor__feature_creation_pipeline__encoder__feature_name_combiner | concat |
| preprocessor__feature_creation_pipeline__encoder__handle_unknown | infrequent_if_exist |
| preprocessor__feature_creation_pipeline__encoder__max_categories | |
| preprocessor__feature_creation_pipeline__encoder__min_frequency | |
| preprocessor__feature_creation_pipeline__encoder__sparse_output | False |
| feature-selection__k | all |
| feature-selection__score_func | <function mutual_info_classif at 0x000001E7EDA4E480> |
| classifier__bootstrap | True |
| classifier__ccp_alpha | 0.0 |
| classifier__class_weight | |
| 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 | 1 |
| classifier__min_samples_split | 2 |
| classifier__min_weight_fraction_leaf | 0.0 |
| classifier__monotonic_cst | |
| classifier__n_estimators | 100 |
| classifier__n_jobs | -1 |
| classifier__oob_score | False |
| classifier__random_state | 2024 |
| classifier__verbose | 0 |
| classifier__warm_start | False |
</details>
### Model Plot
<style>#sk-container-id-17 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
}#sk-container-id-17 {color: var(--sklearn-color-text);
}#sk-container-id-17 pre {padding: 0;
}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;
}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;
}#sk-container-id-17 div.sk-text-repr-fallback {display: none;
}div.sk-parallel-item,
div.sk-serial,
div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
}/* Parallel-specific style estimator block */#sk-container-id-17 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;
}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;
}/* Serial-specific style estimator block */#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-17 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
}/* Toggleable label */
#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
}#sk-container-id-17 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
}/* Toggleable content - dropdown */#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-17 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-17 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
}/* Estimator-specific style *//* Colorize estimator box */
#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
}#sk-container-id-17 div.sk-label label.sk-toggleable__label,
#sk-container-id-17 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
}/* On hover, darken the color of the background */
#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
}/* Label box, darken color on hover, fitted */
#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
}/* Estimator label */#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
}#sk-container-id-17 div.sk-label-container {text-align: center;
}/* Estimator-specific */
#sk-container-id-17 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-17 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
}/* on hover */
#sk-container-id-17 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-17 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
}.sk-estimator-doc-link.fitted,
a:link.sk-estimator-doc-link.fitted,
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
}/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover,
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
}.sk-estimator-doc-link:hover span {display: block;
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-17 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
}#sk-container-id-17 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
}/* On hover */
#sk-container-id-17 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}#sk-container-id-17 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-17" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('numerical_pipeline',Pipeline(steps=[('log_transformations',FunctionTransformer(func=<ufunc 'log1p'>)),('imputer',SimpleImputer(strategy='median')),('scaler',RobustScaler())]),['prg', 'pl', 'pr', 'sk','ts', 'm11', 'bd2', 'age']),('categorical_pipeline',Pipeline(steps=[('as_categorical',Funct...FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='infrequent_if_exist',sparse_output=False))]),['age'])])),('feature-selection',SelectKBest(k='all',score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)),('classifier',RandomForestClassifier(n_jobs=-1, random_state=2024))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-218" type="checkbox" ><label for="sk-estimator-id-218" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('numerical_pipeline',Pipeline(steps=[('log_transformations',FunctionTransformer(func=<ufunc 'log1p'>)),('imputer',SimpleImputer(strategy='median')),('scaler',RobustScaler())]),['prg', 'pl', 'pr', 'sk','ts', 'm11', 'bd2', 'age']),('categorical_pipeline',Pipeline(steps=[('as_categorical',Funct...FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='infrequent_if_exist',sparse_output=False))]),['age'])])),('feature-selection',SelectKBest(k='all',score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)),('classifier',RandomForestClassifier(n_jobs=-1, random_state=2024))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-219" type="checkbox" ><label for="sk-estimator-id-219" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> preprocessor: ColumnTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[('numerical_pipeline',Pipeline(steps=[('log_transformations',FunctionTransformer(func=<ufunc 'log1p'>)),('imputer',SimpleImputer(strategy='median')),('scaler', RobustScaler())]),['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2','age']),('categorical_pipeline',Pipeline(steps=[('as_categorical',FunctionTransformer(func=<function as_...handle_unknown='infrequent_if_exist',sparse_output=False))]),['insurance']),('feature_creation_pipeline',Pipeline(steps=[('feature_creation',FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='infrequent_if_exist',sparse_output=False))]),['age'])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-220" type="checkbox" ><label for="sk-estimator-id-220" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">numerical_pipeline</label><div class="sk-toggleable__content fitted"><pre>['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-221" type="checkbox" ><label for="sk-estimator-id-221" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=<ufunc 'log1p'>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-222" type="checkbox" ><label for="sk-estimator-id-222" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='median')</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-223" type="checkbox" ><label for="sk-estimator-id-223" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RobustScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.RobustScaler.html">?<span>Documentation for RobustScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>RobustScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-224" type="checkbox" ><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">categorical_pipeline</label><div class="sk-toggleable__content fitted"><pre>['insurance']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-225" type="checkbox" ><label for="sk-estimator-id-225" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='most_frequent')</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">feature_creation_pipeline</label><div class="sk-toggleable__content fitted"><pre>['age']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-229" type="checkbox" ><label for="sk-estimator-id-229" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-230" type="checkbox" ><label for="sk-estimator-id-230" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='most_frequent')</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-231" type="checkbox" ><label for="sk-estimator-id-231" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-232" type="checkbox" ><label for="sk-estimator-id-232" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SelectKBest<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k='all',score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-233" type="checkbox" ><label for="sk-estimator-id-233" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(n_jobs=-1, random_state=2024)</pre></div> </div></div></div></div></div></div>
## Evaluation Results
[More Information Needed]
# 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]
```
# citation_bibtex
bibtex
@inproceedings{...,year={2024}}
# get_started_code
import joblib
clf = joblib.load(../models/RandomForestClassifier.joblib)
# model_card_authors
Gabriel Okundaye
# limitations
This model needs further feature engineering to improve the f1 weighted score. Collaborate on with me here [GitHub](https://github.com/D0nG4667/sepsis_prediction_full_stack)
# model_description
This is a RandomForestClassifier model trained on Sepsis dataset from this [kaggle dataset](https://www.kaggle.com/datasets/chaunguynnghunh/sepsis/data).
# roc_auc_curve
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# feature_importances
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