Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
Question 1 - Classification vs. Regression¶
Your goal for this project is to identify students who might need early intervention before they fail to graduate. Which type of supervised learning problem is this, classification or regression? Why?
Answer:
Classfication
Because the output of this dataset(Yes/No) is discrete.
Exploring the Data¶
Run the code cell below to load necessary Python libraries and load the student data. Note that the last column from this dataset, 'passed', will be our target label (whether the student graduated or didn't graduate). All other columns are features about each student.
# Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
Implementation: Data Exploration¶
Let's begin by investigating the dataset to determine how many students we have information on, and learn about the graduation rate among these students. In the code cell below, you will need to compute the following:
- The total number of students,
n_students. - The total number of features for each student,
n_features. - The number of those students who passed,
n_passed. - The number of those students who failed,
n_failed. - The graduation rate of the class,
grad_rate, in percent (%).
student_data.head()
student_data.shape
# TODO: Calculate number of students
n_students = student_data.shape[0]
# TODO: Calculate number of features
n_features = student_data.shape[1] - 1
# TODO: Calculate passing students
n_passed = sum(student_data['passed'] == 'yes')
# TODO: Calculate failing students
n_failed = sum(student_data['passed'] == 'no')
# TODO: Calculate graduation rate
grad_rate = 100. * n_passed / (n_passed + n_failed)
# Print the results
print "Total number of students: {}".format(n_students)
print "Number of features: {}".format(n_features)
print "Number of students who passed: {}".format(n_passed)
print "Number of students who failed: {}".format(n_failed)
print "Graduation rate of the class: {:.2f}%".format(grad_rate)
Preparing the Data¶
In this section, we will prepare the data for modeling, training and testing.
Identify feature and target columns¶
It is often the case that the data you obtain contains non-numeric features. This can be a problem, as most machine learning algorithms expect numeric data to perform computations with.
Run the code cell below to separate the student data into feature and target columns to see if any features are non-numeric.
# Extract feature columns
feature_cols = list(student_data.columns[:-1])
# Extract target column 'passed'
target_col = student_data.columns[-1]
# Show the list of columns
print "Feature columns:\n{}".format(feature_cols)
print "\nTarget column: {}".format(target_col)
# Separate the data into feature data and target data (X_all and y_all, respectively)
X_all = student_data[feature_cols]
y_all = student_data[target_col]
# Show the feature information by printing the first five rows
print "\nFeature values:"
print X_all.head()
Preprocess Feature Columns¶
As you can see, there are several non-numeric columns that need to be converted! Many of them are simply yes/no, e.g. internet. These can be reasonably converted into 1/0 (binary) values.
Other columns, like Mjob and Fjob, have more than two values, and are known as categorical variables. The recommended way to handle such a column is to create as many columns as possible values (e.g. Fjob_teacher, Fjob_other, Fjob_services, etc.), and assign a 1 to one of them and 0 to all others.
These generated columns are sometimes called dummy variables, and we will use the pandas.get_dummies() function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section.
def preprocess_features(X):
''' Preprocesses the student data and converts non-numeric binary variables into
binary (0/1) variables. Converts categorical variables into dummy variables. '''
# Initialize new output DataFrame
output = pd.DataFrame(index = X.index)
# Investigate each feature column for the data
for col, col_data in X.iteritems():
# If data type is non-numeric, replace all yes/no values with 1/0
if col_data.dtype == object:
col_data = col_data.replace(['yes', 'no'], [1, 0])
# If data type is categorical, convert to dummy variables
if col_data.dtype == object:
# Example: 'school' => 'school_GP' and 'school_MS'
col_data = pd.get_dummies(col_data, prefix = col)
# Collect the revised columns
output = output.join(col_data)
return output
X_all = preprocess_features(X_all)
print "Processed feature columns ({} total features):\n{}".format(len(X_all.columns), list(X_all.columns))
Implementation: Training and Testing Data Split¶
So far, we have converted all categorical features into numeric values. For the next step, we split the data (both features and corresponding labels) into training and test sets. In the following code cell below, you will need to implement the following:
- Randomly shuffle and split the data (
X_all,y_all) into training and testing subsets.- Use 300 training points (approximately 75%) and 95 testing points (approximately 25%).
- Set a
random_statefor the function(s) you use, if provided. - Store the results in
X_train,X_test,y_train, andy_test.
# TODO: Import any additional functionality you may need here
from sklearn.cross_validation import StratifiedShuffleSplit
# TODO: Set the number of training points
num_train = 300
# Set the number of testing points
num_test = X_all.shape[0] - num_train
random_seed = 27
# TODO: Shuffle and split the dataset into the number of training and testing points above
sss = StratifiedShuffleSplit(y_all, 1, test_size= num_test * 1.0 / (num_test + num_train), random_state= random_seed)
print "Split length: " , len(sss)
for train_index, test_index in sss:
#print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X_all.iloc[train_index], X_all.iloc[test_index]
y_train, y_test = y_all[train_index], y_all[test_index]
# Show the results of the split
print "Training set has {} samples.".format(X_train.shape[0])
print "Testing set has {} samples.".format(X_test.shape[0])
Training and Evaluating Models¶
In this section, you will choose 3 supervised learning models that are appropriate for this problem and available in scikit-learn. You will first discuss the reasoning behind choosing these three models by considering what you know about the data and each model's strengths and weaknesses. You will then fit the model to varying sizes of training data (100 data points, 200 data points, and 300 data points) and measure the F1 score. You will need to produce three tables (one for each model) that shows the training set size, training time, prediction time, F1 score on the training set, and F1 score on the testing set.
Question 2 - Model Application¶
List three supervised learning models that are appropriate for this problem. What are the general applications of each model? What are their strengths and weaknesses? Given what you know about the data, why did you choose these models to be applied?
Answer:
1. Decision Tree
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
- Advantage: DT is easy to code, beautifully to grow and help interpret data graphically.
- Disadvantage: The disadvantage of DT is that it is prone to overfitting.
- Complexity: In general, the run time cost to construct a balanced binary tree is O(nsamplesnfeatureslog(nsamples)) and query time is O(log(nsamples)).
- DT Algorithms: ID3, C4.5, CART, CHAID, MARS.
Refer https://en.wikipedia.org/wiki/Decision_tree_learning
- Application: DT could be used for multi-output problems, such as face completion with multi-output estimators.
More real word applicaitons are listed in http://www.cbcb.umd.edu/~salzberg/docs/murthy_thesis/survey/node32.html
2. SVM
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
- Advantage: SVM is effective in high dimensional spaces.It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. SVM works really well in complicate domains where there is a clear margin of separation.
- Disadvantage: It does not work well for large dataset since training time is cubic size of the data size. It does not work well with lots of noise when the class are very overlapping and you have to count independent evidence. The data is prone to be overfitting due to the noise.
- Complexity: The core of an SVM is a quadratic programming problem (QP), separating support vectors from the rest of the training data.
- Application: SVM has been used successfully in many real-world problems.
- text (and hypertext) categorization
- image classification
- bioinformatics (Protein classification,Cancer classification)
- hand-written character recognition
3. Naive Bayes
Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features.
- Advantage: Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods. The decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one dimensional distribution. This in turn helps to alleviate problems stemming from the curse of dimensionality. Also, it handles missing data and has good computational complexity.
- Disadvantage: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
- Application: Some of real world examples are as given below:
- To mark an email as spam, or not spam ?
- Classify a news article about technology, politics, or sports ?
- Check a piece of text expressing positive emotions, or negative emotions?
- Also used for face recognition softwares
We have a relative small dataset with relative more features. The data in this project is discrete, and the feature list does not only contain numerical data but categorical data. Therefore, it is approporiate to use these three supervised learning models for classification.
Setup¶
Run the code cell below to initialize three helper functions which you can use for training and testing the three supervised learning models you've chosen above. The functions are as follows:
- takes as input a classifier and training data and fits the classifier to the data.
predict_labels- takes as input a fit classifier, features, and a target labeling and makes predictions using the F1 score.train_predict- takes as input a classifier, and the training and testing data, and performstrain_clasifierandpredict_labels.- This function will report the F1 score for both the training and testing data separately.
def train_classifier(clf, X_train, y_train):
''' Fits a classifier to the training data. '''
# Start the clock, train the classifier, then stop the clock
start = time()
clf.fit(X_train, y_train)
end = time()
# Print the results
print "Trained model in {:.4f} seconds".format(end - start)
def predict_labels(clf, features, target):
''' Makes predictions using a fit classifier based on F1 score. '''
# Start the clock, make predictions, then stop the clock
start = time()
y_pred = clf.predict(features)
end = time()
# Print and return results
print "Made predictions in {:.4f} seconds.".format(end - start)
return f1_score(target.values, y_pred, pos_label='yes')
def train_predict(clf, X_train, y_train, X_test, y_test):
''' Train and predict using a classifer based on F1 score. '''
# Indicate the classifier and the training set size
print "Training a {} using a training set size of {}. ".format(clf.__class__.__name__, len(X_train))
# Train the classifier
train_classifier(clf, X_train, y_train)
# Print the results of prediction for both training and testing
print "F1 score for training set: {:.4f}.".format(predict_labels(clf, X_train, y_train))
print "F1 score for test set: {:.4f}.".format(predict_labels(clf, X_test, y_test))
print "........................................."
Implementation: Model Performance Metrics¶
With the predefined functions above, you will now import the three supervised learning models of your choice and run the train_predict function for each one. Remember that you will need to train and predict on each classifier for three different training set sizes: 100, 200, and 300. Hence, you should expect to have 9 different outputs below — 3 for each model using the varying training set sizes. In the following code cell, you will need to implement the following:
- Import the three supervised learning models you've discussed in the previous section.
- Initialize the three models and store them in
clf_A,clf_B, andclf_C.- Use a
random_statefor each model you use, if provided. - Note: Use the default settings for each model — you will tune one specific model in a later section.
- Use a
- Create the different training set sizes to be used to train each model.
- Do not reshuffle and resplit the data! The new training points should be drawn from
X_trainandy_train.
- Do not reshuffle and resplit the data! The new training points should be drawn from
- Fit each model with each training set size and make predictions on the test set (9 in total).
Note: Three tables are provided after the following code cell which can be used to store your results.
# TODO: Import the three supervised learning models from sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
# TODO: Initialize the three models
clf_A = DecisionTreeClassifier(random_state = random_seed)
clf_B = SVC(random_state = random_seed)
clf_C = GaussianNB()
# TODO: Set up the training set sizes
# TODO: Execute the 'train_predict' function for each classifier and each training set size
for clf in [clf_A, clf_B, clf_C]:
for size in [100, 200, 300]:
train_predict(clf, X_train[:size], y_train[:size], X_test, y_test)
Classifer 1 - Decision Tree
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0012 | 0.0002 | 1.0000 | 0.7612 |
| 200 | 0.0014 | 0.0002 | 1.0000 | 0.7244 |
| 300 | 0.0018 | 0.0002 | 1.0000 | 0.7302 |
Classifer 2 - SVC
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0012 | 0.0009 | 0.8774 | 0.8205 |
| 200 | 0.0032 | 0.0022 | 0.8636 | 0.8052 |
| 300 | 0.0059 | 0.0046 | 0.8584 | 0.7651 |
Classifer 3 - GaussianNB
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0007 | 0.0003 | 0.8201 | 0.7424 |
| 200 | 0.0008 | 0.0004 | 0.8213 | 0.7273 |
| 300 | 0.0008 | 0.0004 | 0.7980 | 0.7556 |
Choosing the Best Model¶
In this final section, you will choose from the three supervised learning models the best model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (X_train and y_train) by tuning at least one parameter to improve upon the untuned model's F1 score.
Question 3 - Chosing the Best Model¶
Based on the experiments you performed earlier, in one to two paragraphs, explain to the board of supervisors what single model you chose as the best model. Which model is generally the most appropriate based on the available data, limited resources, cost, and performance?
Answer:
I chose SVC as the best model.
For decision tree, we could see that the model fit the training data really well, however, the F1 score for test data is pretty low compared with the other two models, indicating that DT is prone to overfitting.
For GuassianNB, the model fit the data well and the F1 score is reasonable, but they are relative lower than those of the SVC model if we consider all limited available data.
Question 4 - Model in Layman's Terms¶
In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. For example if you've chosen to use a decision tree or a support vector machine, how does the model go about making a prediction?
Answer:
SVMs are linear classifiers which assume that the data are linearly separable. Suppose we want to split green dots from red dots in the graph below. There is an infinite number of lines that could achieve this. SVM is to find a line that maximize the minimum distance to either green dots or red dots. How? SVM searches for the closest dots. Once it has found the closest dots, the SVM draws a line connecting them and declares the best separating line to be the line that bisects -- and is perpendicular to -- the connecting line. Why doing this? It is because that if we then introduce new dots in the graph, it is more likely that the old seperation is still good. And those closest points are called supported vectors.
What if the seperation is not a straight line, but a curved one?
SVM achieve this by "lifting" the features we observe into higher dimensions, get the separating plane and convert it back to the lower dimensions. For example, if we can't draw a line in the space, then we may try adding a third dimension. SVM defines the boundary using Support Vectors. They are the ones that are closest to the boundary and "support" the separation. The separating boundary is the optimal boundary.
Implementation: Model Tuning¶
Fine tune the chosen model. Use grid search (GridSearchCV) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:
- Import
sklearn.grid_search.gridSearchCVandsklearn.metrics.make_scorer. - Create a dictionary of parameters you wish to tune for the chosen model.
- Example:
parameters = {'parameter' : [list of values]}.
- Example:
- Initialize the classifier you've chosen and store it in
clf. - Create the F1 scoring function using
make_scorerand store it inf1_scorer.- Set the
pos_labelparameter to the correct value!
- Set the
- Perform grid search on the classifier
clfusingf1_scoreras the scoring method, and store it ingrid_obj. - Fit the grid search object to the training data (
X_train,y_train), and store it ingrid_obj.
# TODO: Import 'GridSearchCV' and 'make_scorer'
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import make_scorer
# TODO: Create the parameters list you wish to tune
parameters = {'C' : list(np.logspace(-10, 10, num=21)),
'kernel': [ 'poly', 'rbf', 'sigmoid'],
'degree': [2, 3, 4]}
# Create the Stratified Shuffle Split object
#sss = StratifiedShuffleSplit(y_train, n_iter=10, test_size=0.24, random_state=ran_state)
# TODO: Initialize the classifier
clf = SVC(random_state = random_seed)
# TODO: Make an f1 scoring function using 'make_scorer'
f1_scorer = make_scorer(f1_score, pos_label="yes")
# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(clf, param_grid = parameters, scoring = f1_scorer)
# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_obj.fit(X_train, y_train)
# Get the estimator
clf = grid_obj.best_estimator_
# Print the parameters
print clf.get_params(), '\n'
# Report the final F1 score for training and testing after parameter tuning
print "Tuned model has a training F1 score of {:.4f}.".format(predict_labels(clf, X_train, y_train))
print "Tuned model has a testing F1 score of {:.4f}.".format(predict_labels(clf, X_test, y_test))
Question 5 - Final F1 Score¶
What is the final model's F1 score for training and testing? How does that score compare to the untuned model?
Answer:
The final model's F1 score is 0.8121 for training and 0.7742 for testing. The score for training decreased but for testing increased.
Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.