In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one over the other.
We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisine.csv_ in the root of this 4-lesson folder.
### Preparation
We have loaded your _notebook.ipynb_ file with the cleaned dataset and have divided it into X and y dataframes, ready for the model building process.
@ -17,6 +19,8 @@ Previously, you learned about the various options you have when classifying data
> Tip: [visit this map online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) and click along the path to read documentation.
### The plan
This map is very helpful once you have a clear grasp of your data, as you can 'walk' along its paths to a decision:
- We have >50 samples
@ -28,50 +32,66 @@ This map is very helpful once you have a clear grasp of your data, as you can 'w
- We can try a ✨ KNeighbors Classifier
- If that doesn't work, try ✨ SVC and ✨ Ensemble Classifiers
This is a very helpful trail to follow. Following this path, we should start by importing some libraries to use:
This is a very helpful trail to follow.
```python
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
import numpy as np
```
Split your training and test data:
## Exercise - split the data
Following this path, we should start by importing some libraries to use.
1. Import the needed libraries:
```python
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
Start by creating an array of classifiers. You will add progressively to this array as we test. Start with a Linear SVC:
Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a 'kernel' to decide how to cluster the labels. The 'C' parameter refers to 'regularization' which regulates the influence of parameters. The kernel can be one of [several](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); here we set it to 'linear' to ensure that we leverage linear SVC. Probability defaults to 'false'; here we set it to 'true' to gather probability estimates. We set the random state to '0' to shuffle the data to get probabilities.
```python
C = 10
# Create different classifiers.
classifiers = {
### Exercise - apply a linear SVC
Start by creating an array of classifiers. You will add progressively to this array as we test.
@ -82,23 +102,27 @@ Accuracy (train) for Linear SVC: 78.6%
accuracy 0.79 1199
macro avg 0.79 0.79 0.79 1199
weighted avg 0.79 0.79 0.79 1199
```
✅ Learn about Linear SVC
weighted avg 0.79 0.79 0.79 1199
```
Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a 'kernel' to decide how to cluster the labels. The 'C' parameter refers to 'regularization' which regulates the influence of parameters. The kernel can be one of [several](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); here we set it to 'linear' to ensure that we leverage linear SVC. Probability defaults to 'false'; here we set it to 'true' to gather probability estimates. We set the random state to '0' to shuffle the data to get probabilities.
## K-Neighbors classifier
The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier. Add a line to your classifier array (add a comma after the Linear SVC item):
K-Neighbors is part of the "neighbors" family of ML methods, which can be used for both supervised and unsupervised learning. In this method, a predefined number of points is created and data are gathered around these points such that generalized labels can be predicted for the data.
```python
'KNN classifier': KNeighborsClassifier(C),
```
The result is a little worse:
### Exercise - apply the K-Neighbors classifier
```
Accuracy (train) for KNN classifier: 73.8%
The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier.
1. Add a line to your classifier array (add a comma after the Linear SVC item):
```python
'KNN classifier': KNeighborsClassifier(C),
```
The result is a little worse:
```output
Accuracy (train) for KNN classifier: 73.8%
precision recall f1-score support
chinese 0.64 0.67 0.66 242
@ -109,22 +133,29 @@ Accuracy (train) for KNN classifier: 73.8%
accuracy 0.74 1199
macro avg 0.76 0.74 0.74 1199
weighted avg 0.76 0.74 0.74 1199
```
✅ Learn about [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)
weighted avg 0.76 0.74 0.74 1199
```
✅ Learn about [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)
K-Neighbors is part of the "neighbors" family of ML methods, which can be used for both supervised and unsupervised learning. In this method, a predefined number of points is created and data are gathered around these points such that generalized labels can be predicted for the data.
## Support Vector Classifier
Let's try for a little better accuracy with a Support Vector Classifier. Add a comma after the K-Neighbors item, and then add this line:
Support-Vector classifiers are part of the [Support-Vector Machine](https://wikipedia.org/wiki/Support-vector_machine) family of ML methods that are used for classification and regression tasks. SVMs "map training examples to points in space" to maximize the distance between two categories. Subsequent data is mapped into this space so their category can be predicted.
```python
'SVC': SVC(),
```
The result is quite good!
### Exercise - apply a Support Vector Classifier
```
Accuracy (train) for SVC: 83.2%
Let's try for a little better accuracy with a Support Vector Classifier.
1. Add a comma after the K-Neighbors item, and then add this line:
```python
'SVC': SVC(),
```
The result is quite good!
```output
Accuracy (train) for SVC: 83.2%
precision recall f1-score support
chinese 0.79 0.74 0.76 242
@ -135,23 +166,23 @@ Accuracy (train) for SVC: 83.2%
accuracy 0.83 1199
macro avg 0.84 0.83 0.83 1199
weighted avg 0.84 0.83 0.83 1199
```
weighted avg 0.84 0.83 0.83 1199
```
✅ Learn about [Support-Vectors](https://scikit-learn.org/stable/modules/svm.html#svm)
✅ Learn about [Support-Vectors](https://scikit-learn.org/stable/modules/svm.html#svm)
Support-Vector classifiers are part of the [Support-Vector Machine](https://wikipedia.org/wiki/Support-vector_machine) family of ML methods that are used for classification and regression tasks. SVMs "map training examples to points in space" to maximize the distance between two categories. Subsequent data is mapped into this space so their category can be predicted.
## Ensemble Classifiers
Let's follow the path to the very end, even though the previous test was quite good. Let's try some 'Ensemble Classifiers, specifically Random Forest and AdaBoost:
```
```python
'RFST': RandomForestClassifier(n_estimators=100),
'ADA': AdaBoostClassifier(n_estimators=100)
```
The result is very good, especially for Random Forest:
```
```output
Accuracy (train) for RFST: 84.5%
precision recall f1-score support
@ -178,6 +209,7 @@ Accuracy (train) for ADA: 72.4%
macro avg 0.73 0.73 0.72 1199
weighted avg 0.73 0.72 0.72 1199
```
✅ Learn about [Ensemble Classifiers](https://scikit-learn.org/stable/modules/ensemble.html)
This method of Machine Learning "combines the predictions of several base estimators" to improve the model's quality. In our example, we used Random Trees and AdaBoost.
@ -187,14 +219,17 @@ This method of Machine Learning "combines the predictions of several base estima
- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) fits a classifier to a dataset and then fits copies of that classifier to the same dataset. It focuses on the weights of incorrectly classified items and adjusts the fit for the next classifier to correct.
---
## 🚀Challenge
Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology!
Jen Looper
4 years ago
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GitHub