In these four lessons, you will explore a fundamental focus of classic machine learning - _classification_. We will walk through using various classification algorithms with a dataset about all the brilliant cuisines of Asia and India. Hope you're hungry!
Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with regression techniques. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: _binary classification_ and _multiclass classification_.
> 🎥 Click the image above for a video: MIT's John Guttag introduces classification
> 🎥 点击上面的图片来观看：MIT的 John Guttag 向您介绍分类
Remember:
请记住：
- **Linear regression** helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict _what price a pumpkin would be in September vs. December_, for example.
- **Logistic regression** helped you discover "binary categories": at this price point, _is this pumpkin orange or not-orange_?
- **逻辑回归** 帮助您发现“二元归类”：在这个价位上，*这个南瓜是橙色还是非橙色* ？
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?"), to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it.
To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.
Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.
Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as `smoker`, `weight`, and `age` to determine _likelihood of developing X disease_. As a supervised learning technique similar to the regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome.
✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
> 🎥 Click the image above for a video.The whole premise of the show 'Chopped' is the 'mystery basket' where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!
The question we want to ask of this cuisine dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?
Scikit-learn offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two lessons, you'll learn about several of these algorithms.
## 练习 - 清理和平衡您的数据
## Exercise - clean and balance your data
The first task at hand, before starting this project, is to clean and **balance** your data to get better results. Start with the blank _notebook.ipynb_ file in the root of this folder.
The first task at hand, before starting this project, is to clean and **balance** your data to get better results. Start with the blank _notebook.ipynb_ file in the root of this folder.
马特浩倪
4 years ago