chapter 06

ch06/README.md

@@ -0,0 +1,55 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
+
+### Chapter Outline
+
+- Streamlining workflows with pipelines
+  - Loading the Breast Cancer Wisconsin dataset
+  - Combining transformers and estimators in a pipeline
+- Using k-fold cross-validation to assess model performance
+  - The holdout method
+  - K-fold cross-validation
+- Debugging algorithms with learning and validation curves
+  - Diagnosing bias and variance problems with learning curves
+  - Addressing over- and underfitting with validation curves
+- Fine-tuning machine learning models via grid search
+  - Tuning hyperparameters via grid search
+  - Algorithm selection with nested cross-validation
+- Looking at different performance evaluation metrics
+  - Reading a confusion matrix
+  - Optimizing the precision and recall of a classification model
+  - Plotting a receiver operating characteristic
+  - Scoring metrics for multiclass classification
+- Dealing with class imbalance
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch06.ipynb`](ch06.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.