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  1. theme.min.css

    }[class^=ai-],[class*=" ai-"],[class^=bi-],[class*=" bi-"]{d...t([type=date]):not([type=datetime-local]):not([type=month]):...
    fess.codelibs.org/_static/assets/css/theme.min.css
    2025-04-16 02:35
      329K bytes
      4 views
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  2. Fess Configuration Properties

    he=hen hi=hin hr=hrn hu=hun hy=hyn id=idn it=itn ja=jan ko=kon...sv=svn ta=tan te=ten th=thn tl=tln tr=trn uk=ukn ur=urn vi=vin...
    fess.codelibs.org/14.19/config/properties.html
    2025-04-16 02:34
      117.2K bytes
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  3. dict-19.png

    PixelInterleaved width=1015, height=5271, bitDepth=8, colorType=RGBAlpha,...red=8, green=8, blue=8, alpha=8 RGB 8 8 8 8 5271 1 1.0 1 8 8 8...
    fess.codelibs.org/ja/_images/dict-19.png
    2025-04-16 02:38
      461.7K bytes
      1 views
     
  4. Comparing different clustering algorithms on to...

    ) # ========== # Create cluster objects # ========== ms = cluster...) # ========== # Set up cluster parameters # ========== plt ....
    scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
    2025-04-16 15:57
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  5. Classification of text documents using sparse f...

    name )) ========== Logistic Regression __________...dimensionality: 5316 density: 1.0 ========== Ridge Classifier __________...
    scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html
    2025-04-16 15:57
      157.5K bytes
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  6. Combine predictors using stacking — scikit-lear...

    (): df = fetch_openml ( name = "house_prices" , as_frame = True...True ) X = df . data y = df . target features = [ "YrSold" , "HeatingQC"...
    scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html
    2025-04-16 15:57
      244K bytes
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  7. Post-tuning the decision threshold for cost-sen...

    mask_false_negative = ( y_true == 1 ) & ( y_pred == 0 ) fraudulent_refuse = mask_true_positive...ax = axs [ 1 ], name = name , plot_chance_level = idx == 1 ,...
    scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html
    2025-04-16 15:57
      242.8K bytes
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  8. Feature transformations with ensembles of trees...

    max_depth = max_depth , random_state = 10 ) _ = gradient_boosting...RandomTreesEmbedding(max_depth=3, n_estimators=10, random_state=0)), ('logisticregression',...
    scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html
    2025-04-16 15:57
      152.2K bytes
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  9. Displaying Pipelines — scikit-learn 1.6.1 docum...

    default is display='diagram' . set_config ( display = "diagram" ) pipe...change to display='text' . set_config ( display = "text" ) pipe...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html
    2025-04-16 15:57
      209.2K bytes
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  10. Comparing Target Encoder with Other Encoders — ...

    wine_reviews = fetch_openml ( data_id = 42074 , as_frame = True ) df...handle_unknown = "ignore" , max_categories = 20 , sparse_output = False...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder.html
    2025-04-16 15:57
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