In this lesson, you will use the dataset you saved from the last lesson full of balanced, clean data all about cuisines.
You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
Scikit-learn groups classification under Supervised Learning, and in that category you will find many ways to classify. [The variety](https://scikit-learn.org/stable/supervised_learning.html) is quite bewildering at first sight. The following methods all include classification techniques:
> You can also use [neural networks to classify data](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), but that is outside the scope of this lesson.
So, which classifier should you choose? Often, running through several and looking for a good result is a way to test. Scikit-learn offers a [side-by-side comparison](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) on a created dataset, comparing KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, showing the results visualized:
> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)
A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:
- **Neural networks are too heavy**. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, neural networks are too heavyweight for this task.
- **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all.
- **Decision tree or logistic regression could work**. A decision tree might work, or logistic regression for multiclass data.
- **Multiclass Boosted Decision Trees solve a different problem**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.
We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
Essentially there are two important parameters - `multi_class` and `solver` - that we need to specify, when we ask Scikit-learn to perform a logistic regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values.
- **Uses the one-vs-rest (OvR) scheme**, if the `multi_class` option is set to `ovr`
- **Uses the cross-entropy loss**, if the `multi_class` option is set to `multinomial`. (Currently the `multinomial` option is supported only by the ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)"
> 🎓 The 'scheme' here can either be 'ovr' (one-vs-rest) or 'multinomial'. Since logistic regression is really designed to support binary classification, these schemes allow it to better handle multiclass classification tasks. [source](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
> 🎓 The 'solver' is defined as "the algorithm to use in the optimization problem". [source](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
Since you are using the multiclass case, you need to choose what _scheme_ to use and what _solver_ to set. Use LogisticRegression with a multiclass setting and the **liblinear** solver to train.
> Note, use Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) function to flatten your data when needed.
In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
Dig a little more into the math behind logistic regression in [this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
