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 300 sec.
- Include only ‘xgboost’ model for training.
- Opt for verbose level 2 to get detailed log.
- 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.
- Create an AutoML instance.
>>> aml = AutoML(task_type="Classification" include=['xgboost'], verbose=2, max_runtime_secs=300)
- Fit training data.
>>> aml.fit(iris_train, iris_train.species)
1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Feature Exploration started ... Data Overview: Total Rows in the data: 120 Total Columns in the data: 6 Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage species INTEGER 120 0 None 0 120 0 0.0 100.0 sepal_length FLOAT 120 0 None 0 120 0 0.0 100.0 id INTEGER 120 0 None 0 120 0 0.0 100.0 sepal_width FLOAT 120 0 None 0 120 0 0.0 100.0 petal_width FLOAT 120 0 None 0 120 0 0.0 100.0 petal_length FLOAT 120 0 None 0 120 0 0.0 100.0 Statistics of Data: func id sepal_length sepal_width petal_length petal_width species min 1 4.3 2 1 0.1 1 std 42.789 0.873 0.45 1.811 0.765 0.82 25% 35.75 5.1 2.8 1.5 0.3 1 50% 75.5 5.8 3 4.35 1.3 2 75% 109.25 6.5 3.325 5.1 1.8 3 max 147 7.9 4.4 6.9 2.5 3 mean 73.592 5.877 3.068 3.746 1.185 1.983 count 120 120 120 120 120 120 Target Column Distribution: Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 3.333333 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Feature Engineering started ... Handling duplicate records present in dataset ... Analysis completed. No action taken. Total time to handle duplicate records: 1.59 sec Handling less significant features from data ... Total time to handle less significant features: 5.89 sec Handling Date Features ... Analysis Completed. Dataset does not contain any feature related to dates. No action needed. Total time to handle date features: 0.00 sec Checking Missing values in dataset ... Analysis Completed. No Missing Values Detected. Total time to find missing values in data: 7.32 sec Imputing Missing Values ... Analysis completed. No imputation required. Time taken to perform imputation: 0.01 sec Performing encoding for categorical columns ... Analysis completed. No categorical columns were found. Time taken to encode the columns: 1.56 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Data preparation started ... Spliting of dataset into training and testing ... Training size : 0.8 Testing size : 0.2 Training data sample sepal_length sepal_width petal_length petal_width species id 5.6 2.8 4.9 2.0 3 11 6.0 2.2 5.0 1.5 3 10 6.3 3.3 4.7 1.6 2 18 5.1 2.5 3.0 1.1 2 13 5.7 2.6 3.5 1.0 2 12 4.9 3.6 1.4 0.1 1 20 5.0 2.0 3.5 1.0 2 14 6.7 3.1 5.6 2.4 3 22 6.3 3.3 6.0 2.5 3 9 5.4 3.9 1.3 0.4 1 17 96 rows X 6 columns Testing data sample sepal_length sepal_width petal_length petal_width species id 4.6 3.1 1.5 0.2 1 114 6.4 2.7 5.3 1.9 3 29 5.4 3.9 1.7 0.4 1 85 5.7 2.9 4.2 1.3 2 31 5.5 4.2 1.4 0.2 1 25 7.7 3.8 6.7 2.2 3 28 6.1 3.0 4.6 1.4 2 84 5.0 3.2 1.2 0.2 1 30 5.6 2.9 3.6 1.3 2 102 6.3 2.9 5.6 1.8 3 95 24 rows X 6 columns Time taken for spliting of data: 11.17 sec Outlier preprocessing ... Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 3.333333 Deleting rows of these columns: ['sepal_width'] result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1713844652180031"'5 Sample of training dataset after removing outlier rows: sepal_length sepal_width petal_length petal_width species id 7.3 2.9 6.3 1.8 3 55 6.7 2.5 5.8 1.8 3 108 6.7 3.3 5.7 2.1 3 87 6.1 2.8 4.0 1.3 2 52 7.2 3.0 5.8 1.6 3 120 7.2 3.6 6.1 2.5 3 26 7.4 2.8 6.1 1.9 3 24 6.2 2.2 4.5 1.5 2 107 5.4 3.0 4.5 1.5 2 71 5.4 3.7 1.5 0.2 1 41 93 rows X 6 columns Time Taken by Outlier processing: 35.26 sec result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1713844313813785"'5 result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1713850192151644"' Checking imbalance data ... Imbalance Not Found. Feature selection using lasso ... feature selected by lasso: ['sepal_length', 'petal_width', 'sepal_width', 'petal_length'] Total time taken by feature selection: 3.57 sec scaling Features of lasso data ... columns that will be scaled: ['sepal_length', 'petal_width', 'sepal_width', 'petal_length'] Training dataset sample after scaling: id species sepal_length petal_width sepal_width petal_length 40 1 0.1818181818181817 0.04166666666666667 0.7777777777777778 0.035087719298245605 80 1 0.2121212121212119 0.08333333333333333 0.7222222222222222 0.035087719298245605 112 1 0.1818181818181817 0.04166666666666667 0.6666666666666666 0.052631578947368425 61 1 0.1818181818181817 0.04166666666666667 0.6111111111111109 0.035087719298245605 26 3 0.8484848484848485 1.0 0.7777777777777778 0.8596491228070174 34 1 0.06060606060606039 0.08333333333333333 0.6666666666666666 0.035087719298245605 78 1 0.12121212121212106 0.04166666666666667 0.5 0.07017543859649125 122 3 0.6363636363636362 0.8750000000000001 0.44444444444444436 0.8070175438596491 17 1 0.30303030303030304 0.12500000000000003 0.9444444444444444 0.017543859649122823 76 1 0.12121212121212106 0.04166666666666667 0.6666666666666666 0.07017543859649125 93 rows X 6 columns Testing dataset sample after scaling: id species sepal_length petal_width sepal_width petal_length 127 3 1.0606060606060606 0.7916666666666666 0.8888888888888887 0.9122807017543859 118 3 0.5757575757575756 0.7083333333333334 0.2777777777777778 0.6491228070175439 99 1 -0.030303030303030467 0.0 0.44444444444444436 -0.017543859649122782 116 3 0.5151515151515149 0.7083333333333334 0.44444444444444436 0.6491228070175439 130 2 0.33333333333333326 0.375 0.11111111111111098 0.43859649122807015 27 2 0.6666666666666665 0.5 0.3888888888888888 0.5964912280701753 85 1 0.30303030303030304 0.12500000000000003 0.9444444444444444 0.08771929824561403 30 1 0.1818181818181817 0.04166666666666667 0.5555555555555556 0.0 101 2 0.48484848484848475 0.375 0.0 0.49122807017543857 93 2 0.48484848484848475 0.625 0.2777777777777778 0.6842105263157894 24 rows X 6 columns Total time taken by feature scaling: 38.49 sec Feature selection using rfe ... feature selected by RFE: ['petal_length', 'petal_width'] Total time taken by feature selection: 10.98 sec scaling Features of rfe data ... columns that will be scaled: ['r_petal_length', 'r_petal_width'] Training dataset sample after scaling: id species r_petal_length r_petal_width 40 1 0.035087719298245605 0.04166666666666667 80 1 0.035087719298245605 0.08333333333333333 112 1 0.052631578947368425 0.04166666666666667 61 1 0.035087719298245605 0.04166666666666667 26 3 0.8596491228070174 1.0 34 1 0.035087719298245605 0.08333333333333333 78 1 0.07017543859649125 0.04166666666666667 122 3 0.8070175438596491 0.8750000000000001 17 1 0.017543859649122823 0.12500000000000003 76 1 0.07017543859649125 0.04166666666666667 93 rows X 4 columns Testing dataset sample after scaling: id species r_petal_length r_petal_width 127 3 0.9122807017543859 0.7916666666666666 118 3 0.6491228070175439 0.7083333333333334 99 1 -0.017543859649122782 0.0 116 3 0.6491228070175439 0.7083333333333334 130 2 0.43859649122807015 0.375 27 2 0.5964912280701753 0.5 85 1 0.08771929824561403 0.12500000000000003 30 1 0.0 0.04166666666666667 101 2 0.49122807017543857 0.375 93 2 0.6842105263157894 0.625 24 rows X 4 columns Total time taken by feature scaling: 35.82 sec scaling Features of pca data ... columns that will be scaled: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] Training dataset sample after scaling: id species sepal_length sepal_width petal_length petal_width 18 2 0.5757575757575756 0.6111111111111109 0.6140350877192983 0.625 52 2 0.5151515151515149 0.33333333333333315 0.49122807017543857 0.5 44 3 0.5151515151515149 0.22222222222222218 0.7719298245614034 0.5416666666666666 55 3 0.8787878787878787 0.3888888888888888 0.894736842105263 0.7083333333333334 120 3 0.8484848484848485 0.44444444444444436 0.8070175438596491 0.625 26 3 0.8484848484848485 0.7777777777777778 0.8596491228070174 1.0 24 3 0.9090909090909092 0.33333333333333315 0.8596491228070174 0.75 107 2 0.5454545454545454 0.0 0.5789473684210525 0.5833333333333334 108 3 0.6969696969696969 0.16666666666666657 0.8070175438596491 0.7083333333333334 87 3 0.6969696969696969 0.6111111111111109 0.7894736842105263 0.8333333333333334 93 rows X 6 columns Testing dataset sample after scaling: id species sepal_length sepal_width petal_length petal_width 91 3 0.7575757575757576 0.5 0.7368421052631579 0.8333333333333334 85 1 0.30303030303030304 0.9444444444444444 0.08771929824561403 0.12500000000000003 114 1 0.06060606060606039 0.5 0.052631578947368425 0.04166666666666667 130 2 0.33333333333333326 0.11111111111111098 0.43859649122807015 0.375 102 2 0.3636363636363634 0.3888888888888888 0.4210526315789474 0.5 31 2 0.3939393939393939 0.3888888888888888 0.5263157894736842 0.5 95 3 0.5757575757575756 0.3888888888888888 0.7719298245614034 0.7083333333333334 28 3 1.0 0.8888888888888887 0.9649122807017544 0.8750000000000001 84 2 0.5151515151515149 0.44444444444444436 0.5964912280701753 0.5416666666666666 30 1 0.1818181818181817 0.5555555555555556 0.0 0.04166666666666667 24 rows X 6 columns Total time taken by feature scaling: 33.92 sec Dimension Reduction using pca ... PCA columns: ['col_0', 'col_1'] Total time taken by PCA: 10.29 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Model Training started ... Hyperparameters used for model training: response_column : species name : xgboost model_type : Classification column_sampling : (1, 0.6) min_impurity : (0.0, 0.1) lambda1 : (0.01, 0.1, 1, 10) shrinkage_factor : (0.5, 0.1, 0.2) max_depth : (5, 6, 7, 8) min_node_size : (1, 2) iter_num : (10, 20) Total number of models for xgboost : 768 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Performing hyperParameter tuning ... xgboost ---------------------------------------------------------------------------------------------------- Evaluating models performance ... Evaluation completed. Leaderboard Rank Model-ID Feature-Selection Accuracy Micro-Precision Micro-Recall Micro-F1 Macro-Precision Macro-Recall Macro-F1 Weighted-Precision Weighted-Recall Weighted-F1 0 1 XGBOOST_2 pca 0.958333 0.958333 0.958333 0.958333 0.962963 0.958333 0.958170 0.962963 0.958333 0.958170 1 2 XGBOOST_5 pca 0.958333 0.958333 0.958333 0.958333 0.962963 0.958333 0.958170 0.962963 0.958333 0.958170 2 3 XGBOOST_1 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 3 4 XGBOOST_4 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 4 5 XGBOOST_7 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 5 6 XGBOOST_0 lasso 0.791667 0.791667 0.791667 0.791667 0.812121 0.791667 0.784076 0.812121 0.791667 0.784076 6 7 XGBOOST_3 lasso 0.791667 0.791667 0.791667 0.791667 0.812121 0.791667 0.784076 0.812121 0.791667 0.784076 7 8 XGBOOST_6 lasso 0.750000 0.750000 0.750000 0.750000 0.857143 0.750000 0.709091 0.857143 0.750000 0.709091 8 rows X 13 columns 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Completed: |⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿| 100% - 15/15
- Display model leaderboard.
>>> aml.leaderboard()
Rank Model-ID Feature-Selection Accuracy Micro-Precision Micro-Recall Micro-F1 Macro-Precision Macro-Recall Macro-F1 Weighted-Precision Weighted-Recall Weighted-F1 0 1 XGBOOST_2 pca 0.958333 0.958333 0.958333 0.958333 0.962963 0.958333 0.958170 0.962963 0.958333 0.958170 1 2 XGBOOST_5 pca 0.958333 0.958333 0.958333 0.958333 0.962963 0.958333 0.958170 0.962963 0.958333 0.958170 2 3 XGBOOST_1 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 3 4 XGBOOST_4 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 4 5 XGBOOST_7 rfe 0.875000 0.875000 0.875000 0.875000 0.878307 0.875000 0.874510 0.878307 0.875000 0.874510 5 6 XGBOOST_0 lasso 0.791667 0.791667 0.791667 0.791667 0.812121 0.791667 0.784076 0.812121 0.791667 0.784076 6 7 XGBOOST_3 lasso 0.791667 0.791667 0.791667 0.791667 0.812121 0.791667 0.784076 0.812121 0.791667 0.784076 7 8 XGBOOST_6 lasso 0.750000 0.750000 0.750000 0.750000 0.857143 0.750000 0.709091 0.857143 0.750000 0.709091
- Display the best performing model.
>>> aml.leader()
Rank Model-ID Feature-Selection Accuracy Micro-Precision Micro-Recall Micro-F1 Macro-Precision Macro-Recall Macro-F1 Weighted-Precision Weighted-Recall Weighted-F1 0 1 XGBOOST_2 pca 0.958333 0.958333 0.958333 0.958333 0.962963 0.958333 0.95817 0.962963 0.958333 0.95817
- Generate prediction on validation dataset using best performing model.In the data preparation phase, AutoML generates the validation dataset by splitting the data provided during fitting into training and testing sets. AutoML's model training utilizes the training data, with the testing data acting as the validation dataset for model evaluation.
>>> prediction = aml.predict()
Following model is being used for generating prediction : Model ID : XGBOOST_2 Feature Selection Method : pca Prediction : id Prediction Confidence_Lower Confidence_upper species 0 27 2 0.625 0.625 2 1 29 3 0.750 0.750 3 2 30 1 1.000 1.000 1 3 31 2 0.625 0.625 2 4 91 3 0.750 0.750 3 5 84 2 0.625 0.625 2 6 130 2 1.000 1.000 2 7 28 3 0.750 0.750 3 8 114 1 1.000 1.000 1 9 25 1 1.000 1.000 1 Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 8 0 0 1.000000 1.000 1.000000 8 2 3 CLASS_3 0 0 7 1.000000 0.875 0.933333 8 1 2 CLASS_2 0 8 1 0.888889 1.000 0.941176 8 Confusion Matrix : array([[8, 0, 0], [0, 8, 0], [0, 1, 7]], dtype=int64)
>>> prediction.head()
id Prediction Confidence_Lower Confidence_upper species 28 3 0.75 0.75 3 30 1 1.0 1.0 1 31 2 0.625 0.625 2 84 2 0.625 0.625 2 91 3 0.75 0.75 3 93 2 0.625 0.625 2 85 1 1.0 1.0 1 29 3 0.75 0.75 3 27 2 0.625 0.625 2 25 1 1.0 1.0 1
- Generate prediction on test dataset using best performing model.
>>> prediction = aml.predict(iris_test)
Data Transformation started ... Performing transformation carried out in feature engineering phase ... Updated dataset after dropping irrelevent columns : sepal_length sepal_width petal_length petal_width species 6.4 2.8 5.6 2.1 3 4.4 2.9 1.4 0.2 1 5.6 3.0 4.1 1.3 2 5.7 2.5 5.0 2.0 3 4.9 2.4 3.3 1.0 2 5.5 2.3 4.0 1.3 2 6.1 2.8 4.7 1.2 2 5.1 3.8 1.6 0.2 1 5.1 3.4 1.5 0.2 1 5.9 3.0 5.1 1.8 3 Updated dataset after performing target column transformation : petal_width sepal_length petal_length sepal_width id species 0.2 4.4 1.4 2.9 9 1 0.2 5.1 1.5 3.4 10 1 1.8 5.9 5.1 3.0 18 3 0.4 5.4 1.5 3.4 15 1 1.0 4.9 3.3 2.4 14 2 1.3 5.5 4.0 2.3 22 2 2.0 5.7 5.0 2.5 12 3 1.4 6.7 4.4 3.1 20 2 2.1 6.4 5.6 2.8 13 3 1.8 6.5 5.5 3.0 21 3 Performing transformation carried out in data preparation phase ... result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1713853197024405"' Updated dataset after performing Lasso feature selection: id sepal_length petal_width sepal_width petal_length species 19 5.1 0.2 3.8 1.6 1 17 5.6 1.3 3.0 4.1 2 34 6.2 2.3 3.4 5.4 3 26 5.1 0.4 3.7 1.5 1 15 5.4 0.4 3.4 1.5 1 32 6.7 2.5 3.3 5.7 3 36 4.8 0.3 3.0 1.4 1 28 5.8 1.2 2.7 3.9 2 38 6.3 1.5 2.8 5.1 3 12 5.7 2.0 2.5 5.0 3 Updated dataset after performing scaling on Lasso selected features : id species sepal_length petal_width sepal_width petal_length 19 1 0.2121212121212119 0.04166666666666667 0.8888888888888887 0.07017543859649125 38 3 0.5757575757575756 0.5833333333333334 0.33333333333333315 0.6842105263157894 12 3 0.3939393939393939 0.7916666666666666 0.16666666666666657 0.6666666666666666 17 2 0.3636363636363634 0.5 0.44444444444444436 0.5087719298245613 22 2 0.33333333333333326 0.5 0.055555555555555365 0.49122807017543857 35 1 0.30303030303030304 0.04166666666666667 0.6666666666666666 0.08771929824561403 15 1 0.30303030303030304 0.12500000000000003 0.6666666666666666 0.052631578947368425 32 3 0.6969696969696969 1.0 0.6111111111111109 0.7894736842105263 36 1 0.12121212121212106 0.08333333333333333 0.44444444444444436 0.035087719298245605 28 2 0.4242424242424241 0.4583333333333333 0.2777777777777778 0.4736842105263158 Updated dataset after performing RFE feature selection: id petal_length petal_width species 15 1.5 0.4 1 22 4.0 1.3 2 35 1.7 0.2 1 38 5.1 1.5 3 36 1.4 0.3 1 28 3.9 1.2 2 19 1.6 0.2 1 30 5.1 2.4 3 17 4.1 1.3 2 34 5.4 2.3 3 Updated dataset after performing scaling on RFE selected features : id species r_petal_length r_petal_width 19 1 0.07017543859649125 0.04166666666666667 38 3 0.6842105263157894 0.5833333333333334 12 3 0.6666666666666666 0.7916666666666666 36 1 0.035087719298245605 0.08333333333333333 22 2 0.49122807017543857 0.5 35 1 0.08771929824561403 0.04166666666666667 17 2 0.5087719298245613 0.5 34 3 0.7368421052631579 0.9166666666666666 15 1 0.052631578947368425 0.12500000000000003 32 3 0.7894736842105263 1.0 Updated dataset after performing scaling for PCA feature selection : id species sepal_length sepal_width petal_length petal_width 22 2 0.33333333333333326 0.055555555555555365 0.49122807017543857 0.5 38 3 0.5757575757575756 0.33333333333333315 0.6842105263157894 0.5833333333333334 12 3 0.3939393939393939 0.16666666666666657 0.6666666666666666 0.7916666666666666 15 1 0.30303030303030304 0.6666666666666666 0.052631578947368425 0.12500000000000003 19 1 0.2121212121212119 0.8888888888888887 0.07017543859649125 0.04166666666666667 30 3 0.4242424242424241 0.33333333333333315 0.6842105263157894 0.9583333333333333 36 1 0.12121212121212106 0.44444444444444436 0.035087719298245605 0.08333333333333333 28 2 0.4242424242424241 0.2777777777777778 0.4736842105263158 0.4583333333333333 17 2 0.3636363636363634 0.44444444444444436 0.5087719298245613 0.5 34 3 0.5454545454545454 0.6666666666666666 0.7368421052631579 0.9166666666666666 Updated dataset after performing PCA feature selection : id col_0 col_1 species 0 26 -0.637053 -0.237890 1 1 17 0.019809 0.057742 2 2 22 0.074623 0.428188 2 3 19 -0.688597 -0.282715 1 4 38 0.298288 0.083267 3 5 36 -0.635618 0.157913 1 6 15 -0.561092 -0.117245 1 7 20 0.219649 -0.117778 2 8 34 0.451922 -0.235519 3 9 35 -0.590568 -0.109948 1 Data Transformation completed. Following model is being used for generating prediction : Model ID : XGBOOST_2 Feature Selection Method : pca Prediction : id Prediction Confidence_Lower Confidence_upper species 0 22 2 0.875 0.875 2 1 38 2 0.500 0.500 3 2 36 1 1.000 1.000 1 3 15 1 1.000 1.000 1 4 34 3 0.750 0.750 3 5 35 1 1.000 1.000 1 6 20 2 0.625 0.625 2 7 19 1 1.000 1.000 1 8 17 2 0.625 0.625 2 9 26 1 1.000 1.000 1 Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 9 0 0 1.000000 1.000000 1.000000 9 2 3 CLASS_3 0 0 9 1.000000 0.818182 0.900000 11 1 2 CLASS_2 0 10 2 0.833333 1.000000 0.909091 10 Confusion Matrix : array([[ 9, 0, 0], [ 0, 10, 0], [ 0, 2, 9]], dtype=int64)
>>> prediction.head()
id Prediction Confidence_Lower Confidence_upper species 10 1 1.0 1.0 1 12 3 0.625 0.625 3 13 3 0.75 0.75 3 14 2 1.0 1.0 2 16 2 0.625 0.625 2 17 2 0.625 0.625 2 15 1 1.0 1.0 1 11 2 0.625 0.625 2 9 1 1.0 1.0 1 8 3 0.75 0.75 3