diff --git a/docs/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb b/docs/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb index 22dbd4f5b..8c3e49c75 100644 --- a/docs/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb +++ b/docs/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb @@ -8,7 +8,7 @@ " \"Open\n", "\n", "\n", - "[View source code](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb) | [Read notebook in online book format](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-bluebook-for-bulldozers-price-regression-v2.ipynb)" + "[View source code](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression-v2.ipynb) | [Read notebook in online book format](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-bluebook-for-bulldozers-price-regression-v2/)" ] }, { @@ -5724,14 +5724,12 @@ "\n", "There are several encoders available for different use cases.\n", "\n", - "TK - does this table show up?\n", - "\n", - "| **Encoder** | **Description** | **Use case** | **For use on** |\n", - "|:-------------|:-----------------|:--------------|:----------------|\n", - "| [LabelEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) | Encode target labels with values between 0 and n_classes-1. | Useful for turning classification target values into numeric representations. | Target labels. |\n", - "| [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#onehotencoder) | Encode categorical features as a [one-hot numeric array](https://en.wikipedia.org/wiki/One-hot). | Turns every positive class of a unique category into a 1 and every negative class into a 0. | Categorical variables/features. |\n", - "| [OrdinalEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#ordinalencoder) | Encode categorical features as an integer array. | Turn unique categorical values into a range of integers, for example, 0 maps to 'cat', 1 maps to 'dog', etc. | Categorical variables/features. |\n", - "| [TargetEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html#targetencoder) | Encode regression and classification targets into a shrunk estimate of the average target values for observations of the category. Useful for converting targets into a certain range of values. | Target variables. |\n", + "| Encoder | Description | Use case | For use on |\n", + "| ----- | ----- | ----- | ----- |\n", + "| [`LabelEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) | Encode target labels with values between 0 and n_classes-1. | Useful for turning classification target values into numeric representations. | Target labels. |\n", + "| [`OneHotEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#onehotencoder) | Encode categorical features as a [one-hot numeric array](https://en.wikipedia.org/wiki/One-hot). | Turns every positive class of a unique category into a 1 and every negative class into a 0. | Categorical variables/features. |\n", + "| [`OrdinalEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#ordinalencoder) | Encode categorical features as an integer array. | Turn unique categorical values into a range of integers, for example, 0 maps to 'cat', 1 maps to 'dog', etc. | Categorical variables/features. |\n", + "| [`TargetEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html#targetencoder) | Encode regression and classification targets into a shrunk estimate of the average target values for observations of the category. | Useful for converting targets into a certain range of values. | Target variables. |\n", "\n", "For our case, we're going to start with `OrdinalEncoder`.\n", "\n", @@ -11405,11 +11403,9 @@ "\n", "Let's now try it on a custom sample.\n", "\n", - "Again, like all good machine learning cooking shows, I've searched the internet for \"bulldozer sales in America\" and [found a sale from 6th July 2024](https://www.purplewave.com/auction/240606/item/EK8504/2004-Caterpillar-D6R_XL-Crawlers-Crawler_Dozer-Missouri) (I'm writing these materials in mid 2024 so if it's many years in the future and the link doesn't work, check out the screenshot below). \n", - "\n", - "TK - image of bulldozer (add raw GitHub link from master)\n", + "Again, like all good machine learning cooking shows, I've searched the internet for \"bulldozer sales in America\" and [found a sale from 6th July 2024](https://www.purplewave.com/auction/240606/item/EK8504/2004-Caterpillar-D6R_XL-Crawlers-Crawler_Dozer-Missouri) (I'm writing these materials in mid 2024 so if it's many years in the future and the link doesn't work, check out the screenshot below).\n", "\n", - "| \"Image | \n", + "| \"Image | \n", "|:--:| \n", "| Screenshot of a bulldozer sale advertisement. I took information from this advertisement to create our own custom sample for testing our machine learning model on data from the wild. [Source](https://www.purplewave.com/auction/240606/item/EK8504/2004-Caterpillar-D6R_XL-Crawlers-Crawler_Dozer-Missouri). |\n", "\n",