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Model commit

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  1. README.md +211 -0
  2. config.json +64 -0
  3. model.pkl +3 -0
README.md ADDED
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+ ---
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+ library_name: quantile-forest
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+ license: apache-2.0
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+ tags:
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+ - quantile-forest
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+ - sklearn
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+ - skops
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+ - tabular-regression
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+ - quantile-regression
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+ - uncertainty-estimation
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+ - prediction-intervals
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+ model_format: pickle
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+ model_file: model.pkl
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+ widget:
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+ - structuredData:
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+ AveBedrms:
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+ - 1.0238095238095237
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+ - 0.9718804920913884
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+ - 1.073446327683616
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+ AveOccup:
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+ - 2.5555555555555554
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+ - 2.109841827768014
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+ - 2.8022598870056497
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+ AveRooms:
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+ - 6.984126984126984
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+ - 6.238137082601054
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+ - 8.288135593220339
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+ HouseAge:
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+ - 41.0
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+ - 21.0
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+ - 52.0
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+ Latitude:
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+ - 37.88
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+ - 37.86
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+ - 37.85
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+ Longitude:
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+ - -122.23
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+ - -122.22
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+ - -122.24
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+ MedInc:
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+ - 8.3252
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+ - 8.3014
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+ - 7.2574
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+ Population:
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+ - 322.0
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+ - 2401.0
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+ - 496.0
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+ ---
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+
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+ # Model description
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+
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+ This is a RandomForestQuantileRegressor trained on the California Housing dataset.
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+
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+ ## Intended uses & limitations
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+
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+ This model is not ready to be used in production.
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+
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+ ## Training Procedure
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+
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+ The model was trained using default parameters on a 5-fold cross-validation pipeline.
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+
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+ ### Hyperparameters
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
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+ |--------------------------|----------------------|
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+ | bootstrap | True |
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+ | ccp_alpha | 0.0 |
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+ | criterion | squared_error |
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+ | default_quantiles | 0.5 |
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+ | max_depth | |
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+ | max_features | 1.0 |
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+ | max_leaf_nodes | |
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+ | max_samples | |
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+ | max_samples_leaf | 1 |
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+ | min_impurity_decrease | 0.0 |
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+ | min_samples_leaf | 1 |
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+ | min_samples_split | 2 |
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+ | min_weight_fraction_leaf | 0.0 |
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+ | monotonic_cst | |
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+ | n_estimators | 100 |
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+ | n_jobs | |
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+ | oob_score | False |
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+ | random_state | RandomState(MT19937) |
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+ | verbose | 0 |
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+ | warm_start | False |
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+
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+ </details>
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+
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+ ### Model Plot
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+
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+ <style>#sk-container-id-1 {/* 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;}
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+ }#sk-container-id-1 {color: var(--sklearn-color-text);
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+ }#sk-container-id-1 pre {padding: 0;
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+ }#sk-container-id-1 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;
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+ }#sk-container-id-1 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);
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+ }#sk-container-id-1 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;
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+ }#sk-container-id-1 div.sk-text-repr-fallback {display: none;
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+ }div.sk-parallel-item,
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+ div.sk-serial,
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+ 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;
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+ }/* Parallel-specific style estimator block */#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
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+ }#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
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+ }#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;
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+ }#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
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+ }#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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+ }#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;
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+ }/* Serial-specific style estimator block */#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
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+ }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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+ clickable and can be expanded/collapsed.
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+ - Pipeline and ColumnTransformer use this feature and define the default style
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+ - Estimators will overwrite some part of the style using the `sk-estimator` class
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+ *//* Pipeline and ColumnTransformer style (default) */#sk-container-id-1 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);
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+ }/* Toggleable label */
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+ #sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
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+ }#sk-container-id-1 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);
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+ }#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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+ }/* Toggleable content - dropdown */#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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+ }#sk-container-id-1 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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+ }#sk-container-id-1 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);
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+ }#sk-container-id-1 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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+ }#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
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+ }#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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+ }/* Pipeline/ColumnTransformer-specific style */#sk-container-id-1 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);
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+ }#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
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+ }/* Estimator-specific style *//* Colorize estimator box */
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+ #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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+ }#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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+ }#sk-container-id-1 div.sk-label label.sk-toggleable__label,
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+ #sk-container-id-1 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
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+ }/* On hover, darken the color of the background */
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+ #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
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+ }/* Label box, darken color on hover, fitted */
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+ #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
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+ }/* Estimator label */#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
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+ }#sk-container-id-1 div.sk-label-container {text-align: center;
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+ }/* Estimator-specific */
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+ #sk-container-id-1 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);
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+ }#sk-container-id-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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+ }/* on hover */
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+ #sk-container-id-1 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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+ }#sk-container-id-1 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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+ }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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+ a:link.sk-estimator-doc-link,
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+ 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);
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+ }.sk-estimator-doc-link.fitted,
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+ a:link.sk-estimator-doc-link.fitted,
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+ a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
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+ }/* On hover */
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+ div.sk-estimator:hover .sk-estimator-doc-link:hover,
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+ .sk-estimator-doc-link:hover,
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+ div.sk-label-container:hover .sk-estimator-doc-link:hover,
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+ .sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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+ }div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
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+ .sk-estimator-doc-link.fitted:hover,
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+ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
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+ .sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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+ }/* Span, style for the box shown on hovering the info icon */
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+ .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);
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+ }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
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+ }.sk-estimator-doc-link:hover span {display: block;
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+ }/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-1 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;
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+ }#sk-container-id-1 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
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+ }/* On hover */
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+ #sk-container-id-1 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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+ }#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
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+ }
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+ </style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>RandomForestQuantileRegressor(random_state=RandomState(MT19937) at 0x129E7B440)</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow ">&nbsp;RandomForestQuantileRegressor<span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></label><div class="sk-toggleable__content "><pre>RandomForestQuantileRegressor(random_state=RandomState(MT19937) at 0x129E7B440)</pre></div> </div></div></div></div>
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+
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+ ## Evaluation Results
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+
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+ | Metric | Value |
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+ |--------------------------------|----------|
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+ | Mean Absolute Percentage Error | 0.164007 |
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+ | Median Absolute Error | 0.171 |
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+ | Mean Squared Error | 0.25832 |
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+ | R-Squared | 0.806 |
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+
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+ # How to Get Started with the Model
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ ```python
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+ from examples.plot_qrf_huggingface_inference import CrossValidationPipeline
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+ pipeline = CrossValidationPipeline.load(qrf_pkl_filename)
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+ ```
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+
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+ </details>
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+
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+ # Model Card Authors
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+
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+ This model card is written by following authors:
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+
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+ [More Information Needed]
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+
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+ # Model Card Contact
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+
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+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
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+ # Citation
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+
206
+ Below you can find information related to citation.
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+
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+ **BibTeX:**
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+ ```
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+ [More Information Needed]
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+ ```
config.json ADDED
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+ {
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+ "sklearn": {
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+ "columns": [
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+ "MedInc",
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+ "HouseAge",
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+ "AveRooms",
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+ "AveBedrms",
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+ "Population",
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+ "AveOccup",
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+ "Latitude",
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+ "Longitude"
12
+ ],
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+ "environment": [
14
+ "quantile-forest=1.3.11"
15
+ ],
16
+ "example_input": {
17
+ "AveBedrms": [
18
+ 1.0238095238095237,
19
+ 0.9718804920913884,
20
+ 1.073446327683616
21
+ ],
22
+ "AveOccup": [
23
+ 2.5555555555555554,
24
+ 2.109841827768014,
25
+ 2.8022598870056497
26
+ ],
27
+ "AveRooms": [
28
+ 6.984126984126984,
29
+ 6.238137082601054,
30
+ 8.288135593220339
31
+ ],
32
+ "HouseAge": [
33
+ 41.0,
34
+ 21.0,
35
+ 52.0
36
+ ],
37
+ "Latitude": [
38
+ 37.88,
39
+ 37.86,
40
+ 37.85
41
+ ],
42
+ "Longitude": [
43
+ -122.23,
44
+ -122.22,
45
+ -122.24
46
+ ],
47
+ "MedInc": [
48
+ 8.3252,
49
+ 8.3014,
50
+ 7.2574
51
+ ],
52
+ "Population": [
53
+ 322.0,
54
+ 2401.0,
55
+ 496.0
56
+ ]
57
+ },
58
+ "model": {
59
+ "file": "model.pkl"
60
+ },
61
+ "model_format": "pickle",
62
+ "task": "tabular-regression"
63
+ }
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+ }
model.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5cea77fadca7d0056a7d3b667aa405d8fd2b021b18af49c02c440c235f2393e2
3
+ size 807486659