This example predicts the species of iris flower based on different factors.
Run AutoML to acquire the most effective model with the following specifications:
- Use early stopping timer to 100 sec.
- Include only 'xgboost' model for training.
- Opt for verbose level 2 to get detailed log.
- Add customization for some specific processes of AutoClassifier.
- Load data and split it to train and test datasets.
- Load the example data and create teradataml DataFrame.
>>> load_example_data("teradataml", "iris_input") - Perform sampling to get 80% for training and 20% for testing.
>>> iris_sample = iris.sample(frac = [0.8, 0.2])
- Fetch train and test data.
>>> iris_train= iris_sample[iris_sample['sampleid'] == 1].drop('sampleid', axis=1)>>> iris_test = iris_sample[iris_sample['sampleid'] == 2].drop('sampleid', axis=1)
- Load the example data and create teradataml DataFrame.
- Add customization.
>>> AutoClassifier.generate_custom_config("custom_iris")Generating custom config JSON for AutoML ... Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 2 Customizing Data Preparation Phase ... Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 1,3 Customizing Data Imbalance Handling ... Available data sampling methods with corresponding indices: Index 1: SMOTE Index 2: NearMiss Enter the corresponding index data imbalance handling method: 1 Customization of data imbalance handling has been completed successfully. Available feature scaling methods with corresponding indices: Index 1: maxabs Index 2: mean Index 3: midrange Index 4: range Index 5: rescale Index 6: std Index 7: sum Index 8: ustd Enter the corresponding index feature scaling method: 6 Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 1 Customization of feature scaling has been completed successfully. Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 4 Customization of data preparation phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 3 Customizing Model Training Phase ... Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 1 Customizing Model Hyperparameter ... Available models for hyperparameter tuning with corresponding indices: Index 1: decision_forest Index 2: xgboost Index 3: knn Index 4: glm Index 5: svm Available hyperparamters update methods with corresponding indices: Index 1: ADD Index 2: REPLACE Enter the list of model indices for performing hyperparameter tuning: 2 Available hyperparameters for model 'xgboost' with corresponding indices: Index 1: min_impurity Index 2: max_depth Index 3: min_node_size Index 4: shrinkage_factor Index 5: iter_num Enter the list of hyperparameter indices for model 'xgboost': 2 Enter the index of corresponding update method for hyperparameters 'max_depth' for model 'xgboost': 2 Enter the list of value for hyperparameter 'max_depth' for model 'xgboost': 3, 4 Customization of model hyperparameter has been completed successfully. Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 3 Customization of model training phase has been completed successfully. Process of generating custom config file for AutoML has been completed successfully. 'custom_iris.json' file is generated successfully under the current working directory.
- Create an AutoML instance.
>>> aml = AutoClassifier(include=['xgboost'], >>> verbose=2, >>> max_runtime_secs=100, >>> custom_config_file='custom_iris.json')
- Fit training data.
>>> aml.fit(iris_train, iris_train.species)
2025-11-04 03:31:31,896 | INFO | Received below input for customization : { "DataImbalanceIndicator": true, "DataImbalanceMethod": "SMOTE", "FeatureScalingIndicator": true, "FeatureScalingParam": { "FeatureScalingMethod": "std", "volatile": true }, "HyperparameterTuningIndicator": true, "HyperparameterTuningParam": { "xgboost": { "max_depth": { "Value": [ 3, 4 ], "Method": "REPLACE" } } } } 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 03:31:31,896 | INFO | Feature Exploration started 2025-11-04 03:31:31,896 | INFO | Data Overview: 2025-11-04 03:31:31,938 | INFO | Total Rows in the data: 120 2025-11-04 03:31:31,960 | INFO | Total Columns in the data: 6 2025-11-04 03:31:32,678 | INFO | Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage 0 petal_length FLOAT 120 0 None 0 120 0 0.0 100.0 1 petal_width FLOAT 120 0 None 0 120 0 0.0 100.0 2 id INTEGER 120 0 None 0 120 0 0.0 100.0 3 species INTEGER 120 0 None 0 120 0 0.0 100.0 4 sepal_width FLOAT 120 0 None 0 120 0 0.0 100.0 5 sepal_length FLOAT 120 0 None 0 120 0 0.0 100.0 2025-11-04 03:31:33,426 | INFO | Statistics of Data: ATTRIBUTE StatName StatValue 0 petal_width MAXIMUM 2.5 1 id MINIMUM 1.0 2 id MAXIMUM 150.0 3 sepal_length COUNT 120.0 4 sepal_length MAXIMUM 7.9 5 petal_length COUNT 120.0 6 petal_length MINIMUM 1.0 7 petal_length MAXIMUM 6.9 8 sepal_length MINIMUM 4.4 9 id COUNT 120.0 2025-11-04 03:31:36,494 | INFO | Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 3.333333 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 03:31:36,793 | INFO | Feature Engineering started ... 2025-11-04 03:31:36,793 | INFO | Handling duplicate records present in dataset ... 2025-11-04 03:31:36,931 | INFO | Analysis completed. No action taken. 2025-11-04 03:31:36,931 | INFO | Total time to handle duplicate records: 0.14 sec 2025-11-04 03:31:36,931 | INFO | Starting customized anti-select columns ... 2025-11-04 03:31:36,931 | INFO | Skipping customized anti-select columns. 2025-11-04 03:31:36,932 | INFO | Handling less significant features from data ... 2025-11-04 03:31:37,642 | INFO | Total time to handle less significant features: 0.71 sec 2025-11-04 03:31:37,642 | INFO | Handling Date Features ... 2025-11-04 03:31:37,642 | INFO | Analysis Completed. Dataset does not contain any feature related to dates. No action needed. 2025-11-04 03:31:37,642 | INFO | Total time to handle date features: 0.00 sec 2025-11-04 03:31:37,642 | INFO | Proceeding with default option for missing value imputation. 2025-11-04 03:31:37,642 | INFO | Proceeding with default option for handling remaining missing values. 2025-11-04 03:31:37,642 | INFO | Checking Missing values in dataset ... 2025-11-04 03:31:39,174 | INFO | Analysis Completed. No Missing Values Detected. 2025-11-04 03:31:39,174 | INFO | Total time to find missing values in data: 1.53 sec 2025-11-04 03:31:39,174 | INFO | Imputing Missing Values ... 2025-11-04 03:31:39,174 | INFO | Analysis completed. No imputation required. 2025-11-04 03:31:39,174 | INFO | Time taken to perform imputation: 0.00 sec 2025-11-04 03:31:39,175 | INFO | No information provided for Variable-Width Transformation. 2025-11-04 03:31:39,175 | INFO | Skipping customized string manipulation. 2025-11-04 03:31:39,175 | INFO | Starting Customized Categorical Feature Encoding ... 2025-11-04 03:31:39,175 | INFO | AutoML will proceed with default encoding technique. 2025-11-04 03:31:39,175 | INFO | Performing encoding for categorical columns ... 2025-11-04 03:31:39,521 | INFO | Analysis completed. No categorical columns were found. 2025-11-04 03:31:39,521 | INFO | Time taken to encode the columns: 0.35 sec 2025-11-04 03:31:39,521 | INFO | Starting customized mathematical transformation ... 2025-11-04 03:31:39,521 | INFO | Skipping customized mathematical transformation. 2025-11-04 03:31:39,522 | INFO | Starting customized non-linear transformation ... 2025-11-04 03:31:39,522 | INFO | Skipping customized non-linear transformation. 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 03:31:39,522 | INFO | Data preparation started ... 2025-11-04 03:31:39,522 | INFO | Starting customized outlier processing ... 2025-11-04 03:31:39,522 | INFO | No information provided for customized outlier processing. AutoML will proceed with default settings. 2025-11-04 03:31:39,522 | INFO | Outlier preprocessing ... 2025-11-04 03:31:42,708 | INFO | Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 3.333333 2025-11-04 03:31:43,301 | INFO | Deleting rows of these columns: ['sepal_width'] 2025-11-04 03:31:45,481 | INFO | Sample of dataset after removing outlier rows: sepal_length sepal_width petal_length petal_width species automl_id id 99 5.1 2.5 3.0 1.1 2 15 97 5.7 2.9 4.2 1.3 2 23 15 5.8 4.0 1.2 0.2 1 27 53 6.9 3.1 4.9 1.5 2 31 30 4.7 3.2 1.6 0.2 1 39 91 5.5 2.6 4.4 1.2 2 43 114 5.7 2.5 5.0 2.0 3 35 36 5.0 3.2 1.2 0.2 1 19 59 6.6 2.9 4.6 1.3 2 11 19 5.7 3.8 1.7 0.3 1 7 116 rows X 7 columns 2025-11-04 03:31:45,592 | INFO | Time Taken by Outlier processing: 6.07 sec 2025-11-04 03:31:45,592 | INFO | Checking imbalance data ... 2025-11-04 03:31:45,674 | INFO | Imbalance Not Found. 2025-11-04 03:31:46,414 | INFO | Feature selection using rfe ... 2025-11-04 03:31:53,335 | INFO | feature selected by RFE: ['id', 'petal_length'] 2025-11-04 03:31:53,337 | INFO | Total time taken by feature selection: 6.92 sec 2025-11-04 03:31:53,616 | INFO | Scaling Features of rfe data ... 2025-11-04 03:31:54,390 | INFO | columns that will be scaled: ['r_id', 'r_petal_length'] 2025-11-04 03:31:55,926 | INFO | Dataset sample after scaling: automl_id species r_id r_petal_length 0 7 1 -1.301357 -1.165924 1 9 1 -1.346574 -1.391996 2 10 3 0.982108 0.699165 3 11 2 -0.397014 0.473093 4 13 3 1.411671 0.586129 5 14 2 -0.442231 0.529611 6 12 2 -0.012669 0.360058 7 8 1 -0.871794 -1.335478 8 6 2 0.077766 -0.148603 9 5 3 1.456888 1.038272 116 rows X 4 columns 2025-11-04 03:31:56,391 | INFO | Total time taken by feature scaling: 2.77 sec 2025-11-04 03:31:56,391 | INFO | Scaling Features of pca data ... 2025-11-04 03:31:56,889 | INFO | columns that will be scaled: ['id', 'sepal_length', 'sepal_width', 'petal_length', 'petal_width'] 2025-11-04 03:31:58,567 | INFO | Dataset sample after scaling: automl_id species id sepal_length sepal_width petal_length petal_width 0 13 3 1.411671 0.174687 -0.099951 0.586129 0.802926 1 10 3 0.982108 0.174687 -2.032346 0.699165 0.404894 2 14 2 -0.442231 0.524062 0.624696 0.529611 0.537572 3 8 1 -0.871794 -1.106352 1.349344 -1.335478 -1.452587 4 16 2 0.371677 -0.058229 -1.066149 0.133986 0.006863 5 7 1 -1.301357 -0.174687 1.832443 -1.165924 -1.187233 6 11 2 -0.397014 0.873436 -0.341501 0.473093 0.139540 7 15 2 0.507328 -0.873436 -1.307698 -0.431192 -0.125815 8 12 2 -0.012669 0.873436 -0.099951 0.360058 0.272217 9 6 2 0.077766 -0.174687 -1.066149 -0.148603 -0.258492 116 rows X 7 columns 2025-11-04 03:31:59,114 | INFO | Total time taken by feature scaling: 2.72 sec 2025-11-04 03:31:59,114 | INFO | Dimension Reduction using pca ... 2025-11-04 03:31:59,749 | INFO | PCA columns: ['col_0', 'col_1', 'col_2'] 2025-11-04 03:31:59,749 | INFO | Total time taken by PCA: 0.63 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 03:32:00,105 | INFO | Model Training started ... 2025-11-04 03:32:00,149 | INFO | Starting customized hyperparameter update ... 2025-11-04 03:32:00,149 | INFO | Completed customized hyperparameter update. 2025-11-04 03:32:00,149 | INFO | Hyperparameters used for model training: 2025-11-04 03:32:00,149 | INFO | Model: xgboost 2025-11-04 03:32:00,149 | INFO | Hyperparameters: {'response_column': 'species', 'name': 'xgboost', 'model_type': 'Classification', 'column_sampling': (1, 0.6), 'min_impurity': (0.0, 0.1), 'lambda1': (1.0, 0.001, 0.01), 'shrinkage_factor': (0.5, 0.1, 0.2), 'max_depth': (3, 4), 'min_node_size': (1, 2), 'iter_num': (10, 20), 'num_boosted_trees': (-1, 2, 5), 'seed': 42} 2025-11-04 03:32:00,149 | INFO | Total number of models for xgboost: 864 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 2025-11-04 03:32:00,149 | INFO | Performing hyperparameter tuning ... 2025-11-04 03:32:01,462 | INFO | Model training for xgboost 2025-11-04 03:32:12,470 | INFO | ---------------------------------------------------------------------------------------------------- 2025-11-04 03:32:12,472 | INFO | Leaderboard RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION ... MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 0 1 XGBOOST_0 rfe 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1 2 XGBOOST_1 pca 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 2 3 XGBOOST_2 rfe 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 3 4 XGBOOST_3 pca 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 [4 rows x 13 columns] 4 rows X 13 columns 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation >>> Completed: |⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿| 100% - 17/17 - Display model leaderboard.
>>> aml.leaderboard()
RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION ... MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 0 1 XGBOOST_0 rfe 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1 2 XGBOOST_1 pca 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 2 3 XGBOOST_2 rfe 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 3 4 XGBOOST_3 pca 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 [4 rows x 13 columns]
- Display the best performing model.
>>> aml.leader()
RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION ... MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 0 1 XGBOOST_0 rfe 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 [1 rows x 13 columns]
- Display model hyperparameters for trained model.
>>> aml.model_hyperparameters(rank=1)
{'response_column': 'species', 'name': 'xgboost', 'model_type': 'Classification', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 1.0, 'shrinkage_factor': 0.5, 'max_depth': 3, 'min_node_size': 1, 'iter_num': 10, 'num_boosted_trees': -1, 'seed': 42, 'persist': False, 'output_prob': True, 'output_responses': ['1', '2', '3']}>>> aml.model_hyperparameters(rank=4)
{'response_column': 'species', 'name': 'xgboost', 'model_type': 'Classification', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 1.0, 'shrinkage_factor': 0.5, 'max_depth': 3, 'min_node_size': 1, 'iter_num': 10, 'num_boosted_trees': 2, 'seed': 42, 'persist': False, 'output_prob': True, 'output_responses': ['1', '2', '3']} - Generate prediction on test dataset using best performing model.
>>> prediction = aml.predict(iris_test)
2025-11-04 03:35:18,195 | INFO | Data Transformation started ... 2025-11-04 03:35:18,195 | INFO | Performing transformation carried out in feature engineering phase ... 2025-11-04 03:35:18,947 | INFO | Updated dataset after performing target column transformation : id sepal_length sepal_width petal_length petal_width species automl_id 0 74 6.1 2.8 4.7 1.2 2 15 1 62 5.9 3.0 4.2 1.5 2 10 2 37 5.5 3.5 1.3 0.2 1 14 3 101 6.3 3.3 6.0 2.5 3 5 4 106 7.6 3.0 6.6 2.1 3 13 5 78 6.7 3.0 5.0 1.7 2 4 6 116 6.4 3.2 5.3 2.3 3 8 7 43 4.4 3.2 1.3 0.2 1 12 8 66 6.7 3.1 4.4 1.4 2 9 9 40 5.1 3.4 1.5 0.2 1 6 30 rows X 7 columns 2025-11-04 03:35:19,802 | INFO | Performing transformation carried out in data preparation phase ... 2025-11-04 03:35:20,930 | INFO | Updated dataset after performing RFE feature selection: id petal_length species automl_id 13 106 6.6 3 5 101 6.0 3 24 18 1.4 1 7 122 4.9 3 12 43 1.3 1 19 117 5.5 3 15 74 4.7 2 30 67 4.5 2 22 92 4.6 2 26 149 5.4 3 30 rows X 4 columns 2025-11-04 03:35:21,953 | INFO | Updated dataset after performing scaling on RFE selected features : automl_id species r_id r_petal_length 0 13 3 0.665588 1.603450 1 15 2 -0.057886 0.529611 2 30 2 -0.216146 0.416576 3 7 3 1.027325 0.642647 4 12 1 -0.758751 -1.391996 5 26 3 1.637756 0.925236 6 5 3 0.552545 1.264343 7 24 1 -1.323965 -1.335478 8 22 2 0.349068 0.473093 9 19 3 0.914282 0.981754 30 rows X 4 columns 2025-11-04 03:35:23,628 | INFO | Updated dataset after performing scaling for PCA feature selection : automl_id species id sepal_length sepal_width petal_length petal_width 0 13 3 0.665588 2.038017 -0.099951 1.603450 1.200958 1 15 2 -0.057886 0.291145 -0.583050 0.529611 0.006863 2 30 2 -0.216146 -0.291145 -0.099951 0.416576 0.404894 3 7 3 1.027325 -0.291145 -0.583050 0.642647 1.068281 4 12 1 -0.758751 -1.688643 0.383147 -1.391996 -1.319910 5 26 3 1.637756 0.407603 0.866246 0.925236 1.466313 6 5 3 0.552545 0.524062 0.624696 1.264343 1.731667 7 24 1 -1.323965 -0.873436 1.107795 -1.335478 -1.187233 8 22 2 0.349068 0.291145 -0.099951 0.473093 0.272217 9 19 3 0.914282 0.756978 -0.099951 0.981754 0.802926 30 rows X 7 columns 2025-11-04 03:35:24,048 | INFO | Updated dataset after performing PCA feature selection : automl_id col_0 col_1 col_2 species 0 26 1.974797 1.035059 1.242515 3 1 17 0.480275 -0.147331 -0.514093 2 2 7 1.362878 -0.583179 0.807251 3 3 19 1.706582 0.268332 0.131765 3 4 5 1.866732 0.932092 0.324474 3 5 34 1.928278 0.322988 -0.003286 3 6 22 0.699742 0.048328 0.010950 2 7 15 0.512768 -0.395743 -0.464033 2 8 24 -2.565497 0.582561 -0.071086 1 9 13 2.679685 0.832049 -0.857995 3 10 rows X 5 columns 2025-11-04 03:35:24,336 | INFO | Data Transformation completed.⫿⫿⫿⫿⫿| 100% - 14/14 2025-11-04 03:35:24,880 | INFO | Following model is being picked for evaluation: 2025-11-04 03:35:24,880 | INFO | Model ID : XGBOOST_0 2025-11-04 03:35:24,880 | INFO | Feature Selection Method : rfe 2025-11-04 03:35:25,406 | INFO | Applying SHAP for Model Interpretation... 2025-11-04 03:35:28,354 | INFO | SHAP Analysis Completed. Feature Importance Available. /root/automl_testing/pyTeradata/teradataml/automl/model_evaluation.py:380: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown plt.show() 2025-11-04 03:35:28,475 | INFO | Prediction : automl_id Prediction species prob_1 prob_2 prob_3 0 30 2 2 0.007360 0.985279 0.007362 1 5 3 3 0.006796 0.006882 0.986322 2 24 1 1 0.985980 0.007056 0.006964 3 17 2 2 0.007360 0.985279 0.007362 4 13 3 3 0.006796 0.006882 0.986322 5 7 3 3 0.006796 0.006882 0.986322 6 22 2 2 0.007360 0.985279 0.007362 7 12 1 1 0.985980 0.007056 0.006964 8 34 3 3 0.006796 0.006882 0.986322 9 26 3 3 0.006796 0.006882 0.986322 2025-11-04 03:35:29,400 | INFO | Confusion Matrix : [[ 8 0 0] [ 0 12 0]>>> prediction.head()
automl_id Prediction species prob_1 prob_2 prob_3 0 13 3 3 0.006796 0.006882 0.986322 1 5 3 3 0.006796 0.006882 0.986322 2 24 1 1 0.985980 0.007056 0.006964 3 19 3 3 0.006796 0.006882 0.986322 4 30 2 2 0.007360 0.985279 0.007362 5 7 3 3 0.006796 0.006882 0.986322 6 22 2 2 0.007360 0.985279 0.007362 7 12 1 1 0.985980 0.007056 0.006964 8 15 2 2 0.007360 0.985279 0.007362 9 26 3 3 0.006796 0.006882 0.986322
- Generate evaluation metrics on test dataset using best performing model.
>>> performance_metrics = aml.evaluate(iris_test)
2025-11-04 03:40:14,901 | INFO | Skipping data transformation as data is already transformed. 2025-11-04 03:40:15,431 | INFO | Following model is being picked for evaluation: 2025-11-04 03:40:15,431 | INFO | Model ID : XGBOOST_0 2025-11-04 03:40:15,431 | INFO | Feature Selection Method : rfe 2025-11-04 03:40:18,047 | INFO | Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 8 0 0 1.0 1.0 1.0 8 2 3 CLASS_3 0 0 10 1.0 1.0 1.0 10 1 2 CLASS_2 0 12 0 1.0 1.0 1.0 12 -------------------------------------------------------------------------------- SeqNum Metric MetricValue 0 3 Micro-Recall 1.0 1 5 Macro-Precision 1.0 2 6 Macro-Recall 1.0 3 7 Macro-F1 1.0 4 9 Weighted-Recall 1.0 5 10 Weighted-F1 1.0 6 8 Weighted-Precision 1.0 7 4 Micro-F1 1.0 8 2 Micro-Precision 1.0 9 1 Accuracy 1.0>>> performance_metrics
Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 8 0 0 1.0 1.0 1.0 8 2 3 CLASS_3 0 0 10 1.0 1.0 1.0 10 1 2 CLASS_2 0 12 0 1.0 1.0 1.0 12
- Generate prediction on test dataset using second best performing model.
>>> prediction = aml.predict(iris_test,2)
2025-11-04 03:54:52,175 | INFO | Skipping data transformation as data is already transformed. 2025-11-04 03:54:52,719 | INFO | Following model is being picked for evaluation: 2025-11-04 03:54:52,720 | INFO | Model ID : XGBOOST_1 2025-11-04 03:54:52,720 | INFO | Feature Selection Method : pca 2025-11-04 03:54:53,246 | INFO | Applying SHAP for Model Interpretation... 2025-11-04 03:54:55,151 | INFO | SHAP Analysis Completed. Feature Importance Available. /root/automl_testing/pyTeradata/teradataml/automl/model_evaluation.py:380: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown plt.show() 2025-11-04 03:54:55,260 | INFO | Prediction : automl_id Prediction species prob_1 prob_2 prob_3 0 7 3 3 0.031410 0.016004 0.952586 1 5 3 3 0.007274 0.007357 0.985369 2 34 3 3 0.007274 0.007357 0.985369 3 22 2 2 0.007344 0.984609 0.008048 4 24 1 1 0.986968 0.007161 0.005871 5 13 3 3 0.007274 0.007357 0.985369 6 15 2 2 0.007358 0.986465 0.006177 7 19 3 3 0.007274 0.007357 0.985369 8 17 2 2 0.007358 0.986465 0.006177 9 26 3 3 0.045905 0.039178 0.914917 2025-11-04 03:54:55,596 | INFO | Confusion Matrix : [[ 8 0 0] [ 0 12 0] [ 0 0 10]]
>>> prediction.head()
automl_id Prediction species prob_1 prob_2 prob_3 0 7 3 3 0.031410 0.016004 0.952586 1 5 3 3 0.007274 0.007357 0.985369 2 34 3 3 0.007274 0.007357 0.985369 3 22 2 2 0.007344 0.984609 0.008048 4 24 1 1 0.986968 0.007161 0.005871 5 13 3 3 0.007274 0.007357 0.985369 6 15 2 2 0.007358 0.986465 0.006177 7 19 3 3 0.007274 0.007357 0.985369 8 17 2 2 0.007358 0.986465 0.006177 9 26 3 3 0.045905 0.039178 0.914917
- Generate evaluation metrics on test dataset using second best performing model.
>>> performance_metrics = aml.evaluate(iris_test,2)
2025-11-04 03:55:32,151 | INFO | Skipping data transformation as data is already transformed. 2025-11-04 03:55:32,702 | INFO | Following model is being picked for evaluation: 2025-11-04 03:55:32,703 | INFO | Model ID : XGBOOST_1 2025-11-04 03:55:32,703 | INFO | Feature Selection Method : pca 2025-11-04 03:55:35,199 | INFO | Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 8 0 0 1.0 1.0 1.0 8 2 3 CLASS_3 0 0 10 1.0 1.0 1.0 10 1 2 CLASS_2 0 12 0 1.0 1.0 1.0 12 -------------------------------------------------------------------------------- SeqNum Metric MetricValue 0 3 Micro-Recall 1.0 1 5 Macro-Precision 1.0 2 6 Macro-Recall 1.0 3 7 Macro-F1 1.0 4 9 Weighted-Recall 1.0 5 10 Weighted-F1 1.0 6 8 Weighted-Precision 1.0 7 4 Micro-F1 1.0 8 2 Micro-Precision 1.0 9 1 Accuracy 1.0>>> performance_metrics
Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 8 0 0 1.0 1.0 1.0 8 2 3 CLASS_3 0 0 10 1.0 1.0 1.0 10 1 2 CLASS_2 0 12 0 1.0 1.0 1.0 12