overfitting& imbalanced data links

Author: nibaccam

articles/machine-learning/concept-automated-ml.md

@@ -109,7 +109,7 @@ In every automated machine learning experiment, your data is preprocessed using
 
 ### Automatic preprocessing (standard)
 
-In every automated machine learning experiment, your data is automatically scaled or normalized to help algorithms perform well.  During model training, one of the following scaling or normalization techniques will be applied to each model.
+In every automated machine learning experiment, your data is automatically scaled or normalized to help algorithms perform well.  During model training, one of the following scaling or normalization techniques will be applied to each model. Learn how autoML helps [prevent over-fitting and imbalanced data](concept-manage-ml-pitfalls.md) in your models.
 
 |Scaling & normalization| Description |
 | ------------- | ------------- |

articles/machine-learning/concept-manage-ml-pitfalls.md

@@ -53,17 +53,17 @@ In the context of automated ML, the first three items above are **best-practices
 
 Using **more data** is the simplest and best possible way to prevent over-fitting, and as an added bonus typically increases accuracy. When you use more data, it becomes harder for the model to memorize exact patterns, and it is forced to reach solutions that are more flexible to accommodate more conditions. It's also important to recognize **statistical bias**, to ensure your training data doesn't include isolated patterns that won't exist in live-prediction data. This scenario can be difficult to solve, because there may not be over-fitting between your train and test sets, but there may be over-fitting present when compared to live test data.
 
-Target leakage is a similar issue, where you may not see over-fitting between train/test sets, but rather it appears at prediction-time. Target leakage occurs when your model "cheats" during training by having access to data that it shouldn't normally have at prediction-time. For example, if your problem is to predict on Monday what a commodity price will be on Friday, but one of your features accidentally included data from Thursdays, that would be data the model won't have at prediction-time since it cannot see into the future. Target leakage is an easy mistake to miss, but is often characterized by abnormally high accuracy for your problem. If you are attempting to predict stock price and trained a model at 95% accuracy, there is likely target leakage somewhere in your features.
+**Target leakage** is a similar issue, where you may not see over-fitting between train/test sets, but rather it appears at prediction-time. Target leakage occurs when your model "cheats" during training by having access to data that it shouldn't normally have at prediction-time. For example, if your problem is to predict on Monday what a commodity price will be on Friday, but one of your features accidentally included data from Thursdays, that would be data the model won't have at prediction-time since it cannot see into the future. Target leakage is an easy mistake to miss, but is often characterized by abnormally high accuracy for your problem. If you are attempting to predict stock price and trained a model at 95% accuracy, there is likely target leakage somewhere in your features.
 
-Removing features can also help with over-fitting by preventing the model from having too many fields to use to memorize specific patterns, thus causing it to be more flexible. It can be difficult to measure quantitatively, but if you can remove features and retain the same accuracy, you have likely made the model more flexible and have reduced the risk of over-fitting.
+**Removing features** can also help with over-fitting by preventing the model from having too many fields to use to memorize specific patterns, thus causing it to be more flexible. It can be difficult to measure quantitatively, but if you can remove features and retain the same accuracy, you have likely made the model more flexible and have reduced the risk of over-fitting.
 
 ### Best practices automated ML implements
 
-Regularization is the process of minimizing a cost function to penalize complex and over-fitted models. There are different types of regularization functions, but in general they all penalize model coefficient size, variance, and complexity. Automated ML uses L1 (Lasso), L2 (Ridge), and ElasticNet (L1 and L2 simultaneously) in different combinations with different model hyperparameter settings that control over-fitting. In simple terms, automated ML will vary how much a model is regulated and choose the best result.
+**Regularization** is the process of minimizing a cost function to penalize complex and over-fitted models. There are different types of regularization functions, but in general they all penalize model coefficient size, variance, and complexity. Automated ML uses L1 (Lasso), L2 (Ridge), and ElasticNet (L1 and L2 simultaneously) in different combinations with different model hyperparameter settings that control over-fitting. In simple terms, automated ML will vary how much a model is regulated and choose the best result.
 
-Automated ML also implements explicit model complexity limitations to prevent over-fitting. In most cases this implementation is specifically for decision tree or forest algorithms, where individual tree max-depth is limited, and the total number of trees used in forest or ensemble techniques are limited.
+Automated ML also implements explicit **model complexity limitations** to prevent over-fitting. In most cases this implementation is specifically for decision tree or forest algorithms, where individual tree max-depth is limited, and the total number of trees used in forest or ensemble techniques are limited.
 
-Cross-validation (CV) is the process of taking many subsets of your full training data and training a model on each subset. The idea is that a model could get "lucky" and have great accuracy with one subset, but by using many subsets the model won't achieve this high accuracy every time. When doing CV, you provide a validation holdout dataset, specify your CV folds (number of subsets) and automated ML will train your model and tune hyperparameters to minimize error on your validation set. One CV fold could be over-fit, but by using many of them it reduces the probability that your final model is over-fit. The tradeoff is that CV does result in longer training times and thus greater cost, because instead of training a model once, you train it once for each *n* CV subsets. 
+**Cross-validation (CV)** is the process of taking many subsets of your full training data and training a model on each subset. The idea is that a model could get "lucky" and have great accuracy with one subset, but by using many subsets the model won't achieve this high accuracy every time. When doing CV, you provide a validation holdout dataset, specify your CV folds (number of subsets) and automated ML will train your model and tune hyperparameters to minimize error on your validation set. One CV fold could be over-fit, but by using many of them it reduces the probability that your final model is over-fit. The tradeoff is that CV does result in longer training times and thus greater cost, because instead of training a model once, you train it once for each *n* CV subsets. 
 
 > [!NOTE]
 > Cross-validation is not enabled by default; it must be configured in automated ML settings. However, after cross-validation is configured and a validation data set has been provided, the process is automated for you. See 

articles/machine-learning/how-to-auto-train-forecast.md

@@ -112,7 +112,7 @@ For time series forecasting Rolling Origin Cross Validation (ROCV) is used to sp
 
 ![alt text](./media/how-to-auto-train-forecast/ROCV.svg)
 
-This strategy will preserve the time series data integrity and eliminate the risk of data leakage. ROCV is automatically used for forecasting tasks by passing the training and validation data together and setting the number of cross validation folds using `n_cross_validations`. 
+This strategy will preserve the time series data integrity and eliminate the risk of data leakage. ROCV is automatically used for forecasting tasks by passing the training and validation data together and setting the number of cross validation folds using `n_cross_validations`. Learn more about how auto ML applies cross validation to [prevent over-fitting models](concept-manage-ml-pitfalls.md#prevent-over-fitting).
 
 ```python
 automl_config = AutoMLConfig(task='forecasting',

articles/machine-learning/how-to-configure-auto-train.md

@@ -118,6 +118,7 @@ You can specify separate train and validation sets directly in the `AutoMLConfig
 
 Use `n_cross_validations` setting to specify the number of cross validations. The training data set will be randomly split into `n_cross_validations` folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for `n_cross_validations` rounds until each fold is used once as validation set. The average scores across all `n_cross_validations` rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 
+Learn more about how autoML applies cross validation to [prevent over-fitting models](concept-manage-ml-pitfalls.md#prevent-over-fitting).
 ### Monte Carlo Cross Validation (Repeated Random Sub-Sampling)
 
 Use `validation_size` to specify the percentage of the training dataset that should be used for validation, and use `n_cross_validations` to specify the number of cross validations. During each cross validation round, a subset of size `validation_size` will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores across all `n_cross_validations` rounds will be reported, and the corresponding model will be retrained on the whole training data set. Monte Carlo is not supported for time series forecasting.

articles/machine-learning/how-to-use-automated-ml-for-ml-models.md

@@ -155,7 +155,7 @@ Kurtosis| Measure of how heavily tailed this column's data is compared to a norm
 
 ## Advanced featurization options
 
-Automated machine learning offers preprocessing and data guardrails automatically, to help you identify and manage potential issues with your data. 
+Automated machine learning offers preprocessing and data guardrails automatically, to help you identify and manage potential issues with your data, like [over-fitting and imbalanced data](concept-manage-ml-pitfalls.md#prevent-over-fitting). 
 
 ### Preprocessing