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@@ -280,7 +280,7 @@ Copy the notebook URL in your internet browser and select the Workflow_PyPEF.ipy
 ## Modeling Techniques
 <a name="pure-ml"></a>
 ### Pure Machine Learning (ML)-based Modeling
-Serveral linear and non-linear modeling options are available by default to construct supervised regression models based on the generated sequence features, i.e. encoded sequences.
+Several linear and non-linear modeling options are available by default to construct supervised regression models based on the generated sequence features, i.e. encoded sequences.
 Regression models are trained, i.e. model hyperparameters are optimized, by *k*- fold (by default, fivefold) cross-validation on training samples. Here, the model aims to map the encoded variant sequences that are the features (***X***) for predicting the corresponding fitness labels (***y***) such that *f(***X***)* --> ***y*** – while cross-validation and/or using a model implementing a penalty will be necessary for better model generalization behavior.
 Following regression options from [Scikit-learn](https://scikit-learn.org/stable/) are implemented (for optimized hyperparameters, see Model Hyperparameters section below):
 - [Partial Least Squares Regression (linear model)](https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html)