Model Prediction (with Copy-Paste)
Once a model is trained, the most valuable thing you can do is use it to predict new data.
The traditional approach is a lot of hassle: organize the data into a file → import → match column names → run the prediction → export again. The StarWay Data Insight platform compresses this whole chain into a single action — Ctrl+V.
🚪 Entry
Top toolbar → Model Prediction (prediction icon)
⚠️ Prerequisite: You must have a model that is activated and fitted (Fit) in the model list on the right. When no usable model exists, this button cannot be clicked.
📋 Interface Layout
When opened, it is a large dialog divided into three sections from top to bottom:
1. Model information bar
| Displayed item | Description |
|---|---|
| Instance | The name of the current instance |
| Model | The current model name + the model type tag (PCA / PLS / PLS-DA / OPLS / OPLS-DA) |
| Metrics | The model's core evaluation metrics; hover the mouse to see explanations |
What the metrics bar displays depends on the model type:
| Model type | Displayed metrics |
|---|---|
| PLS / OPLS (regression) | R² : … | Q² : … |
| PLS-DA / OPLS-DA (classification) | Acc: … | AUC: … | F1: … |
| PCA | Unsupervised model; no prediction metrics are displayed |
💡 Take a look at the metrics before predicting: if Q² is only 0.3, the prediction results themselves are of limited credibility, so don't push your conclusions too far.
2. Data table area
- The first column is the row number index
- Then come the X columns (the header shows two rows: the upper row is the variable's English name, the lower row is the Chinese name)
- Then the original Y columns (if you paste the true values along with the data, they can be used for on-the-spot validation)
- Finally the prediction columns, with a header such as
x3 (Predicted)
3. Prediction comparison chart area
After you click "Predict", each Y variable automatically generates a prediction comparison chart, with evaluation metrics displayed in real time above the chart:
| Model type | Chart metrics |
|---|---|
| Regression | RMSE (root mean square error), R² (coefficient of determination), MAE (mean absolute error) |
| Classification | Acc (accuracy), F1 (macro F1 score), Precision (macro precision) |
💡 Hover over any metric to see a plain-language explanation. For example, R² = "how well the prediction model fits the real data; the closer to 1, the better".
📥 How to Feed Data In: Copy and Paste
This is the only input method for the prediction panel — there is no need to import a file.
Steps
- In Excel, select the data range you want to predict and copy it with Ctrl+C / Cmd+C
- Go back to the prediction dialog and click once inside the dialog area (so that the dialog gets focus)
- Paste with Ctrl+V / Cmd+V
- When you see the "Successfully pasted N rows of data" message, it is done
- Click Predict
⚠️ The single most critical rule: the column order must match
The platform reads pasted data by column order, and does not match by column name. Therefore:
Make sure the column order of the pasted data is exactly the same as the order of the X variable list (and the Y variable list).
The figure above gives explicit hints:
- PCA models: "Please click inside this area, then use Ctrl+V / Cmd+V to paste the X variable data from Excel, making sure the column order matches the X variable list."
- Other models: "…paste data, making sure the column order matches the X and Y variable lists."
Paste format requirements
- Simply copy directly from Excel (tab-separated); there is no need to save it as CSV
- Pasting multiple rows at once is supported, for batch prediction
- The number of columns should correspond to the number of X (+ Y) variables
💡 Recommended practice: rearrange the new data in Excel into the same column order as the prediction panel's header before copying, which avoids order misalignment.
📤 Output Results: Paste Back to Excel in One Click
After the prediction is complete, click Copy Results:
- The results are written to the system clipboard, with the message "Results copied to the clipboard; you can paste them directly into Excel"
- Go back to Excel and press Ctrl+V to paste the predicted values back into your original table
This forms a closed loop: Excel → paste into the platform → predict → paste back into Excel, with no intermediate files produced at any point.
Other actions
| Button | Function |
|---|---|
| Clear | Clears the currently pasted data and prediction results |
| Predict | Runs the prediction on the data in the table |
💡 If the pasted data also contains the true Y values, RMSE / R² (or Acc / F1) are calculated and displayed automatically after prediction — effectively an external validation done along the way.
🔄 Reverse Prediction: Working Back from the Target to the Parameters
Besides "predicting Y from X", the platform also provides a reverse calculation capability: given the principal component scores of a target, it back-calculates the corresponding raw variable values.
This capability has no separate button; it is integrated into the Explore Mode of Model Exploration:
- Drag the red dot on the score plot to the target position
- The X-Space / Y-Space panel below displays the theoretical values of each variable in real time
💡 This is where the answer to "to reach this quality metric, what should the process parameters be set to" comes from. See Model Exploration and Parameter Optimization for details.
🎯 Typical Use Cases
Use case 1: Quick judgment of incoming materials
Receive the test data of a new batch of raw materials → copy it in X column order → paste it into the prediction panel → predict → copy the results backUse case 2: Offline data re-validation
Paste in historical data (including the true Y) → predict → look at R² / RMSE → evaluate the model's actual performance on that batchUse case 3: Multi-target prediction
When a PLS model has multiple Y columns, each Y gets its own comparison chart and its own set of metricsUse case 4: Reverse optimization
Model Exploration → Explore Mode → drag the red dot to the target quality region → read X-Space → get the recommended process parameters⚠️ Notes
- Order-sensitive: a misaligned column order will not raise an error, but it will produce completely wrong prediction results — this is the most common usage accident
- Do not paste the header row: paste only data rows; column names are not recognized
- Numeric format: make sure the cells are numbers rather than text (in Excel, left-aligned "numbers" are often text)
- PCA models can only show structure: an unsupervised model has no Y, so the prediction panel is mainly used to view the projection of samples in the latent space
- Extrapolation risk: if the value range of the new data clearly exceeds that of the training data, the credibility of the prediction drops — refer to the boundary violations (Violations) hints in Model Exploration
🔗 Related Reading
- Model Exploration and Parameter Optimization —— Reverse prediction and the optimization engine
- Step 4: Result Output —— Package and download the model to do batch prediction in your own Python environment
- Prediction Scatter Plot / Prediction Line Chart / Residual Distribution Plot —— Visualization of prediction quality inside the model