Minor typo in "Bayesian Additive Regression Trees: Introduction" note…

…book #584 (#585) * Fix typo in BART_introduction.myst.md Change "season" to "reason" * Fix typo in BART_introduction.ipynb Changes "season" to "reason" in BART_introduction.ipynb --------- Co-authored-by: jmellor <[email protected]>
pymc-devs · Oct 18, 2023 · e6eecdb · e6eecdb
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diff --git a/examples/case_studies/BART_introduction.ipynb b/examples/case_studies/BART_introduction.ipynb
@@ -1037,7 +1037,7 @@
    "id": "2b680d91",
    "metadata": {},
    "source": [
-    "This plot helps us understand the season behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model."
+    "This plot helps us understand the reason behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model."
    ]
   },
   {

diff --git a/examples/case_studies/BART_introduction.myst.md b/examples/case_studies/BART_introduction.myst.md
@@ -389,7 +389,7 @@ ax.set(
 );
 ```
 
-This plot helps us understand the season behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model.
+This plot helps us understand the reason behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model.
 
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@@ Expand Up / @@ -389,7 +389,7 @@ ax.set( @@
     );
     ```
-    This plot helps us understand the season behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model.
+    This plot helps us understand the reason behind the bad performance on the test set: Recall that in the variable importance ranking from the initial model we saw that `hour` was the most important predictor. On the other hand, our training data just sees `hour` values until $19$ (since is our train-test threshold). As BART learns how to partition the (training) data, it can not differentiate between `hour` values between $20$ and $22$ for example. It just cares that both values are greater that $19$. This is very important to understand when using BART! This explains why one should not use BART for time series forecasting if there is a trend component. In this case it is better to detrend the data first, model the remainder with BART and model the trend with a different model.
     +++
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