> ## Documentation Index > Fetch the complete documentation index at: https://docs.getinteract.com/llms.txt > Use this file to discover all available pages before exploring further. # Model Results > Explore channel contributions, response curves, model health diagnostics, and predictive accuracy for your latest Meridian MMM run. The Model Results page displays the output of your most recently trained Meridian model. Results are loaded automatically — as soon as the pipeline writes a new artifact to GCS, it will appear the next time the page is opened. ## How results are discovered Interact scans the configured GCS folder on every page load and loads the latest `model_*.json` file. No manual registration is required. If your pipeline has published multiple runs, a **run selector** appears in the top-right corner so you can browse historical results. The folder scanned is set in **Insights → Settings → MMM** under "Results Folder". The default is `{label}/results` for multi-market configs, or `results` for single-market. *** ## Page sections ### Summary strip The top of the page shows a quick summary of the run: | Item | Description | | ----------------------- | ------------------------------------------------------------------------- | | **KPI** | The outcome variable the model was trained on (e.g. `revenue_eur`) | | **Date range** | Start and end dates of the training dataset | | **Channels** | Number of media channels included in the model | | **Observations** | Total number of data points (weeks or days) | | **Model health** | Health score (0–100) and pass/fail status from Meridian diagnostic checks | | **Predictive accuracy** | Test-set R² at a glance | *** ### Model health Expand the **Model Health** section to see a composite score and individual Meridian diagnostic checks. These tell you how reliably the model was estimated. | Diagnostic | What it measures | Threshold | | --------------------------- | ------------------------------------------------------------------------------- | -------------------------------- | | **Score** | Overall quality score aggregating all checks | 0–100 (higher is better) | | **Convergence (R-hat max)** | Convergence of MCMC chains — the parameter name with the highest R-hat is shown | \< 1.05 to pass | | **Negative baseline** | Posterior probability that the baseline contribution went below zero | ≈ 0 to pass | | **Bayesian PPP** | Bayesian posterior predictive p-value | Not too extreme (e.g. 0.05–0.95) | | **Goodness of fit** | R², MAPE, and wMAPE on overall, training, and hold-out data | Within acceptable ranges | | **ROI consistency** | Optional check on ROI plausibility across channels | `null` when not computed | | **Prior/posterior shift** | Optional check for unexpected prior influence on posteriors | `null` when not computed | A failing health check doesn't necessarily mean the results are wrong, but they should be treated with caution. Common causes are insufficient warm-up steps, poorly specified priors, or a very short time series. Consult your modeller before acting on results from a failing run. *** ### Predictive accuracy This section shows how well the model predicts outcomes on data it did **not** see during training (the hold-out test set). | Metric | Description | | ---------------- | ---------------------------------------------------------------------------------------- | | **R² (test)** | Proportion of variance explained on the test set. Values above 0.8 are generally strong. | | **MAPE (test)** | Mean Absolute Percentage Error on the test set. Lower is better. | | **WMAPE (test)** | Weighted MAPE — less sensitive to periods with very small actual values. | | **R² (train)** | Same metrics on the training set, shown for comparison. | A large gap between training and test R² can indicate overfitting. A MAPE above \~25% on the test set suggests the model has limited predictive power on unseen data. *** ### Actual vs. fitted chart The time-series chart shows three lines across the full modelling period: * **Actual** — the observed KPI values * **Fitted** — the model's predictions * **Baseline** — the portion of the KPI explained by non-media factors (seasonality, organic, etc.) Use this chart to visually assess fit quality. Large, systematic gaps between actual and fitted suggest a structural misfit in the model. *** ### Channel contributions table The table lists every media channel with its aggregate results over the modelling period: | Column | Description | | ----------------------- | ------------------------------------------------------------------------------- | | **Spend** | Total spend attributed to this channel | | **Incremental outcome** | Total KPI units driven by this channel's spend | | **Contribution %** | Share of total KPI attributed to this channel | | **ROI** | Return on investment (`incremental outcome / spend`) | | **mROI** | Marginal ROI — the incremental return at the channel's current spend level | | **CPiK** | Cost per incremental KPI unit | | **Adstock α** | Geometric decay rate (carry-over effect); shown when available | | **Saturation EC50** | Spend level at which 50% of maximum saturation is reached; shown when available | **mROI** is the most actionable metric for budget decisions. A channel with `mROI > 1.0` can absorb more budget profitably; a channel with `mROI < 1.0` is at or past its saturation point. *** ### Response curves Expand the **Response Curves** section to see an S-curve (or diminishing-returns curve) for each channel. The curve shows the predicted incremental outcome at different spend levels, holding all other channels constant. **How to read the chart:** * The **dot** marks the channel's actual spend level from the modelling period * The shaded band (when available) shows the 90% credible interval around the curve * A steep slope at the current spend level = high marginal returns; a flat slope = near saturation Use response curves to compare channels by marginal efficiency and to identify where additional spend has the highest expected return. *** ## Switching between runs If your pipeline has published multiple runs to the same GCS folder, a **run selector** dropdown appears in the top-right corner of the results page. Select any previous run to load its artifact. Runs are labelled by their timestamp (the date and time the pipeline exported the file). The most recent run is always loaded by default. *** ## Settings reference | Setting | Where to configure | What it affects | | ------------------ | ------------------------- | --------------------------------------------------------------- | | **Results Folder** | Insights → Settings → MMM | Which GCS folder is scanned for artifacts | | **Label** | Insights → Settings → MMM | Which data book's results folder to use in multi-market configs | Configure the GCS folder and permissions for model result artifacts.