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cacauvicosa
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heart1ohr2x9e-target-classification

Tabular Classification
Scikit-learn
baseline-trainer
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  1. README.md +72 -0
  2. clf.pkl +0 -0
  3. logs.txt +37 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: sklearn
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+ tags:
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+ - tabular-classification
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+ - baseline-trainer
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+ ---
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+
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+ ## Baseline Model trained on heart1ohr2x9e to apply classification on target
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+
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+ **Metrics of the best model:**
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+
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+ accuracy 0.885854
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+
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+ average_precision 0.949471
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+
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+ roc_auc 0.050633
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+
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+ recall_macro 0.885324
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+
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+ f1_macro 0.885610
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+
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+ Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64
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+
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+
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+
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+ **See model plot below:**
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+
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+ <style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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 the default 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-8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
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+ age False False False ... False False False
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+ sex False False False ... False False False
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+ cp False False False ... False False False
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+ trestbps True False False ... False False False
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+ chol True False False ... False False False
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+ fbs False False False ... False False False
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+ restecg False Fa...... False False False
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+ thalach True False False ... False False False
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+ exang False False False ... False False False
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+ oldpeak True False False ... False False False
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+ slope False False False ... False False False
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+ ca False False False ... False False False
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+ thal False False False ... False False False[13 rows x 7 columns])),(&#x27;logisticregression&#x27;,LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;,max_iter=1000))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
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+ age False False False ... False False False
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+ sex False False False ... False False False
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+ cp False False False ... False False False
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+ trestbps True False False ... False False False
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+ chol True False False ... False False False
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+ fbs False False False ... False False False
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+ restecg False Fa...... False False False
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+ thalach True False False ... False False False
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+ exang False False False ... False False False
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+ oldpeak True False False ... False False False
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+ slope False False False ... False False False
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+ ca False False False ... False False False
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+ thal False False False ... False False False[13 rows x 7 columns])),(&#x27;logisticregression&#x27;,LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
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+ age False False False ... False False False
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+ sex False False False ... False False False
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+ cp False False False ... False False False
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+ trestbps True False False ... False False False
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+ chol True False False ... False False False
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+ fbs False False False ... False False False
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+ restecg False False False ... False False False
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+ thalach True False False ... False False False
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+ exang False False False ... False False False
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+ oldpeak True False False ... False False False
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+ slope False False False ... False False False
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+ ca False False False ... False False False
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+ thal False False False ... False False False[13 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000)</pre></div></div></div></div></div></div></div>
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+ **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
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+ **Logs of training** including the models tried in the process can be found in logs.txt
clf.pkl ADDED
Binary file (10.4 kB). View file
 
logs.txt ADDED
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+ Logging training
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+ Running DummyClassifier()
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+ accuracy: 0.513 average_precision: 0.487 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.339
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+ === new best DummyClassifier() (using recall_macro):
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+ accuracy: 0.513 average_precision: 0.487 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.339
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+
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+ Running GaussianNB()
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+ accuracy: 0.592 average_precision: 0.669 roc_auc: 0.824 recall_macro: 0.602 f1_macro: 0.534
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+ === new best GaussianNB() (using recall_macro):
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+ accuracy: 0.592 average_precision: 0.669 roc_auc: 0.824 recall_macro: 0.602 f1_macro: 0.534
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+
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+ Running MultinomialNB()
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+ accuracy: 0.857 average_precision: 0.934 roc_auc: 0.931 recall_macro: 0.856 f1_macro: 0.856
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+ === new best MultinomialNB() (using recall_macro):
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+ accuracy: 0.857 average_precision: 0.934 roc_auc: 0.931 recall_macro: 0.856 f1_macro: 0.856
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+
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+ Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
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+ accuracy: 0.749 average_precision: 0.680 roc_auc: 0.749 recall_macro: 0.749 f1_macro: 0.749
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+ Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
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+ accuracy: 0.883 average_precision: 0.943 roc_auc: 0.940 recall_macro: 0.882 f1_macro: 0.882
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+ === new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
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+ accuracy: 0.883 average_precision: 0.943 roc_auc: 0.940 recall_macro: 0.882 f1_macro: 0.882
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+
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+ Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
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+ accuracy: 0.833 average_precision: 0.857 roc_auc: 0.878 recall_macro: 0.832 f1_macro: 0.833
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+ Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
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+ accuracy: 0.873 average_precision: 0.941 roc_auc: 0.060 recall_macro: 0.872 f1_macro: 0.873
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+ Running LogisticRegression(class_weight='balanced', max_iter=1000)
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+ accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886
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+ === new best LogisticRegression(class_weight='balanced', max_iter=1000) (using recall_macro):
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+ accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886
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+
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+
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+ Best model:
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+ LogisticRegression(class_weight='balanced', max_iter=1000)
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+ Best Scores:
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+ accuracy: 0.886 average_precision: 0.949 roc_auc: 0.051 recall_macro: 0.885 f1_macro: 0.886