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README.md

@@ -36,66 +36,52 @@ from pycaret.datasets import get_data
 juice = get_data('juice')
 ```
 
-Initializing the pycaret environment setup
+1. Initializing the pycaret environment setup
 
 ```python
 exp1 = setup(juice, 'Purchase')
 ```
 
-Creating a simple logistic regression (includes fitting, CV and metric evaluation)
+2. Creating a simple logistic regression (includes fitting, CV and metric evaluation)
 ```python
 lr = create_model('lr')
 ```
 
 List of available estimators:
 
-Estimator                   Abbreviated String     Original Implementation 
----------                   ------------------     -----------------------
-Logistic Regression         'lr'                   linear_model.LogisticRegression
-K Nearest Neighbour         'knn'                  neighbors.KNeighborsClassifier
-Naives Bayes                'nb'                   naive_bayes.GaussianNB
-Decision Tree               'dt'                   tree.DecisionTreeClassifier
-SVM (Linear)                'svm'                  linear_model.SGDClassifier
-SVM (RBF)                   'rbfsvm'               svm.SVC
-Gaussian Process            'gpc'                  gaussian_process.GPC
-Multi Level Perceptron      'mlp'                  neural_network.MLPClassifier
-Ridge Classifier            'ridge'                linear_model.RidgeClassifier
-Random Forest               'rf'                   ensemble.RandomForestClassifier
-Quadratic Disc. Analysis    'qda'                  discriminant_analysis.QDA
-AdaBoost                    'ada'                  ensemble.AdaBoostClassifier
-Gradient Boosting           'gbc'                  ensemble.GradientBoostingClassifier
-Linear Disc. Analysis       'lda'                  discriminant_analysis.LDA
-Extra Trees Classifier      'et'                   ensemble.ExtraTreesClassifier
-Extreme Gradient Boosting   'xgboost'              xgboost.readthedocs.io
-Light Gradient Boosting     'lightgbm'             github.com/microsoft/LightGBM
-
-Tuning a model using GridSearchCV with pre-defined grids.
-```python
-tuned_lr = tune_model('lr')
-```
-Ensembling trained model
+Logistic Regression (lr)
+K Nearest Neighbour (knn)
+Naive Bayes (nb)
+Decision Tree (dt)
+Support Vector Machine - Linear (svm)
+SVM Radial Function (rbfsvm)
+Gaussian Process Classifier (gpc)
+Multi Level Perceptron (mlp)
+Ridge Classifier (ridge)
+Random Forest (rf)
+Quadtratic Discriminant Analysis (qda)
+Adaboost (ada)
+Gradient Boosting Classifier (gbc)
+Linear Discriminant Analysis (lda)
+Extra Trees Classifier (et)
+Extreme Gradient Boosting - xgboost (xgboost)
+Light Gradient Boosting - Microsoft LightGBM (lightgbm)
+
+3. Tuning a model using inbuilt grids.
 ```python
-dt = create_model('dt')
-dt_bagging = ensemble_model('dt', method='Bagging')
-dt_boosting = ensemble_model('dt', method='Boosting')
+tuned_xgb = tune_model('xgboost')
 ```
 
-Creating a voting classifier
+4. Ensembling Model
 ```python
-voting_clf1 = blend_models() #creates voting classifier for entire library
-
-#create voting classifier for specific models
-lr = create_model('lr')
-svm = create_model('svm')
-mlp = create_model('mlp')
-xgboost = create_model('xgboost')
-
-voting_clf2 = blend_models( [lr, svm, mlp, xgboost] )
+dt = create_model('dt')
+dt_bagging = ensemble_model(dt, method='Bagging')
+dt_boosting = ensemble_model(dt, method='Boosting')
 ```
 
-Creating a voting classifier
+5. Creating a voting classifier
 ```python
-voting_clf1 = blend_models() #creates voting classifier for entire library
+voting_all = blend_models() #creates voting classifier for entire library
 
 #create voting classifier for specific models
 lr = create_model('lr')
@@ -103,10 +89,10 @@ svm = create_model('svm')
 mlp = create_model('mlp')
 xgboost = create_model('xgboost')
 
-voting_clf2 = blend_models( [lr, svm, mlp, xgboost] )
+voting_clf2 = blend_models( [ lr, svm, mlp, xgboost ] )
 ```
 
-Stacking Models in one layer
+6. Stacking Models in Single Layer
 ```python
 #create individual classifiers
 lr = create_model('lr')
@@ -117,7 +103,7 @@ xgboost = create_model('xgboost')
 stacker = stack_models( [lr,svm,mlp], meta_model = xgboost )
 ```
 
-Stacking Models in Multiple layers
+7. Stacking Models in Multiple Layers
 ```python
 #create individual classifiers
 lr = create_model('lr')
@@ -133,49 +119,63 @@ stacknet = create_stacknet( [ [lr,svm,mlp], [gbc, nb], [lightgbm, knn] ], meta_m
 #meta model by default is Logistic Regression
 ```
 
-Plot Models
+8. Plot Models
 ```python
 lr = create_model('lr')
 plot_model(lr, plot='auc')
 ```
 List of available plots:
 
-Name                        Abbreviated String     Original Implementation 
----------                   ------------------     -----------------------
-Area Under the Curve         'auc'                 .. / rocauc.html
-Discrimination Threshold     'threshold'           .. / threshold.html
-Precision Recall Curve       'pr'                  .. / prcurve.html
-Confusion Matrix             'confusion_matrix'    .. / confusion_matrix.html
-Class Prediction Error       'error'               .. / class_prediction_error.html
-Classification Report        'class_report'        .. / classification_report.html
-Decision Boundary            'boundary'            .. / boundaries.html
-Recursive Feat. Selection    'rfe'                 .. / rfecv.html
-Learning Curve               'learning'            .. / learning_curve.html
-Manifold Learning            'manifold'            .. / manifold.html
-Calibration Curve            'calibration'         .. / calibration_curve.html
-Validation Curve             'vc'                  .. / validation_curve.html
-Dimension Learning           'dimension'           .. / radviz.html
-Feature Importance           'feature'                   N/A 
-Model Hyperparameter         'parameter'                 N/A 
-
-Saving Model for Deployment
+Area Under the Curve (auc) 
+Discrimination Threshold (threshold)
+Precision Recall Curve (pr)
+Confusion Matrix (confusion_matrix)
+Class Prediction Error (error)
+Classification Report (class_report) 
+Decision Boundary (boundary)
+Recursive Feature Selection (rfe)
+Learning Curve (learning)
+Manifold Learning (manifold)
+Calibration Curve (calibration)
+Validation Curve (vc)
+Dimension Learning (dimension)
+Feature Importance (feature) 
+Model Hyperparameter (parameter)
+
+9. Evaluate Model
+```python
+lr = create_model('lr')
+evaluate_model(lr) #displays user interface for interactive plotting
+```
+
+10. Interpret Tree Based Models
+```python
+xgboost = create_model('xgboost')
+interpret_model(xgboost)
+```
+
+11. Saving Model for Deployment
 ```python
 lr = create_model('lr')
 save_model(lr, 'lr_23122019')
 ```
-Saving Entire Experiment Pipeline
+
+12. Saving Entire Experiment Pipeline
 ```python
 save_experiment('expname1')
 ```
-Loading Model / Experiment
+
+13. Loading Model / Experiment
 ```python
 m = load_model('lr_23122019')
 e = load_experiment('expname1')
 ```
-AutoML
+
+14. AutoML
 ```python
 aml1 = automl()
 ```
+
 ## Documentation
 Documentation work is in progress. They will be uploaded on our website http://www.pycaret.org as soon as they are available. (Target Availability : 21/01/2020)