Upload dataset
CSV, TSV, TXT, DAT, XLS, XLSX, or XLSM. The first row must contain column names.
GENERAL MACHINE LEARNING
Train models directly from your dataset.
No E-Nose feature extraction. Load a tabular dataset, configure preprocessing, train, evaluate, and export the report.
Ready
Machine-learning task
Choose how the dataset will be modeled.
Feature columns
Numeric columns are selected automatically. You can exclude columns before training.
This version trains on numeric predictors. Categorical target columns are supported for classification; non-numeric predictor columns are excluded to avoid hidden encoding and data leakage.
Missing values
Choose the rule used inside every training fold.
Transform & scaler
Transformation is applied first, then scaling is fitted using training data only.
Dataset
→Mean imputation
→No transform
→StandardScaler
→ML
Principal Component Analysis (PCA)
Unsupervised projection of the selected feature matrix before machine learning.
PCA has not been calculated.
Linear Discriminant Analysis (LDA)
Supervised projection for classification. Class labels are used only in this LDA analysis.
LDA has not been calculated.
PCA and LDA are diagnostic visualizations. Machine-learning training continues to use the selected original features unless you explicitly change the feature selection.
Model
Select an algorithm and its main parameters.
Validation
The test set stays independent from cross-validation.
Preprocessing parameters are fitted separately inside each training fold to reduce leakage.
Hyperparameter Tuning
Optional model selection using cross-validation on the training partition only.
Search spaceComma-separated candidate values. Grid Search evaluates every combination; Random Search samples combinations.
0 combinationsTuning runs only inside the training partition. The independent test set remains untouched until the final model evaluation.
Ready to train
Review the pipeline then train the model. Hyperparameter tuning is optional.
No model trained yet.Complete steps 1–4 and train a model.
MODEL EVALUATION
Training result
Evaluation metrics
Complete aggregate metrics for the independent test set.