student_intervention


Project 2: Supervised Learning

Building a Student Intervention System

Classification vs Regression

Your goal is to identify students who might need early intervention - which type of supervised machine learning problem is this, classification or regression? Why?

Answer: I think this type of problem is classification, because the target data is discrete, students who passed and students who failed.

Exploring the Data

Let's go ahead and read in the student dataset first.


In [1]:
# Import libraries
import numpy as np
import pandas as pd

In [2]:
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns


Student data read successfully!

Now, can you find out the following facts about the dataset?

  • Total number of students
  • Number of students who passed
  • Number of students who failed
  • Graduation rate of the class (%)
  • Number of features

In [3]:
n_students = student_data.shape[0]
n_features = student_data.shape[1]
n_passed = student_data[student_data['passed']=='yes'].shape[0]
n_failed = n_students - n_passed
grad_rate = (n_passed * 100.0) / n_students
print "Total number of students: {}".format(n_students)
print "Number of students who passed: {}".format(n_passed)
print "Number of students who failed: {}".format(n_failed)
print "Number of features: {}".format(n_features-1)
print "Graduation rate of the class: {:.2f}%".format(grad_rate)


Total number of students: 395
Number of students who passed: 265
Number of students who failed: 130
Number of features: 30
Graduation rate of the class: 67.09%

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.


In [4]:
# Extract feature (X) and target (y) columns
feature_cols = list(student_data.columns[:-1])  # all columns but last are features
target_col = student_data.columns[-1]  # last column is the target/label
print "Feature column(s):-\n{}".format(feature_cols)
print "Target column: {}".format(target_col)

X_all = student_data[feature_cols]  # feature values for all students
y_all = student_data[target_col]  # corresponding targets/labels
print "\nFeature values:-"
print X_all.head()  # print the first 5 rows


Feature column(s):-
['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu', 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']
Target column: passed

Feature values:-
  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \
0     GP   F   18       U     GT3       A     4     4  at_home   teacher   
1     GP   F   17       U     GT3       T     1     1  at_home     other   
2     GP   F   15       U     LE3       T     1     1  at_home     other   
3     GP   F   15       U     GT3       T     4     2   health  services   
4     GP   F   16       U     GT3       T     3     3    other     other   

    ...    higher internet  romantic  famrel  freetime goout Dalc Walc health  \
0   ...       yes       no        no       4         3     4    1    1      3   
1   ...       yes      yes        no       5         3     3    1    1      3   
2   ...       yes      yes        no       4         3     2    2    3      3   
3   ...       yes      yes       yes       3         2     2    1    1      5   
4   ...       yes       no        no       4         3     2    1    2      5   

  absences  
0        6  
1        4  
2       10  
3        2  
4        4  

[5 rows x 30 columns]

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.


In [5]:
# Preprocess feature columns
def preprocess_features(X):
    outX = pd.DataFrame(index=X.index)  # output dataframe, initially empty

    # Check each column
    for col, col_data in X.iteritems():
        # If data type is non-numeric, try to replace all yes/no values with 1/0
        if col_data.dtype == object:
            col_data = col_data.replace(['yes', 'no'], [1, 0])
        # Note: This should change the data type for yes/no columns to int

        # If still non-numeric, convert to one or more dummy variables
        if col_data.dtype == object:
            col_data = pd.get_dummies(col_data, prefix=col)  # e.g. 'school' => 'school_GP', 'school_MS'

        outX = outX.join(col_data)  # collect column(s) in output dataframe

    return outX

X_all = preprocess_features(X_all)
print "Processed feature columns ({}):-\n{}".format(len(X_all.columns), list(X_all.columns))


Processed feature columns (48):-
['school_GP', 'school_MS', 'sex_F', 'sex_M', 'age', 'address_R', 'address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T', 'Medu', 'Fedu', 'Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services', 'Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other', 'Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home', 'reason_other', 'reason_reputation', 'guardian_father', 'guardian_mother', 'guardian_other', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']

Split data into training and test sets

So far, we have converted all categorical features into numeric values. In this next step, we split the data (both features and corresponding labels) into training and test sets.


In [6]:
from sklearn.cross_validation import train_test_split
# First, decide how many training vs test samples you want
num_all = student_data.shape[0]  # same as len(student_data)
num_train = 300  # about 75% of the data
num_test = num_all - num_train


# TODO: Then, select features (X) and corresponding labels (y) for the training and test sets
# Note: Shuffle the data or randomly select samples to avoid any bias due to ordering in the dataset
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, train_size=num_train)
print "Training set: {} samples".format(X_train.shape[0])
print "Test set: {} samples".format(X_test.shape[0])
# Note: If you need a validation set, extract it from within training data


Training set: 300 samples
Test set: 95 samples

Training and Evaluating Models

Choose 3 supervised learning models that are available in scikit-learn, and appropriate for this problem. For each model:

  • What are the general applications of this model? What are its strengths and weaknesses?
  • Given what you know about the data so far, why did you choose this model to apply?
  • Fit this model to the training data, try to predict labels (for both training and test sets), and measure the F1 score. Repeat this process with different training set sizes (100, 200, 300), keeping test set constant.

Produce a table showing training time, prediction time, F1 score on training set and F1 score on test set, for each training set size.

Note: You need to produce 3 such tables - one for each model.

Decision Tree Model

  • Strengths:
    • It's easy to understand and to interpret.
    • The cost of predicting data is logarithmic in the number of data points used to train the tree.
    • Able to handle both numerical and categorical data.
  • Weakness:
    • It's very sensitive to slight variations in the data.
    • It's easy to be overfit
  • Why did you choose this model to apply?
    • Because we only have small number of data set and many features, the simple decision tree model could be the good choice.

In [7]:
# Train a Decision Tree model
import time

def train_classifier(clf, X_train, y_train):
    start = time.time()
    clf.fit(X_train, y_train)
    end = time.time()
    return clf, end - start

# Choose a model, import it and instantiate an object
from sklearn import tree
clf = tree.DecisionTreeClassifier()

# Fit model to training data
clf,_ = train_classifier(clf, X_train, y_train)  # note: using entire training set here
print clf


DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

In [8]:
# Predict on training set and compute F1 score
from sklearn.metrics import f1_score

def predict_labels(clf, features, target):
    start = time.time()
    y_pred = clf.predict(features)
    end = time.time()
    return f1_score(target.values, y_pred, pos_label='yes'), end - start

train_f1_score, _ = predict_labels(clf, X_train, y_train)
print "F1 score for training set: {}".format(train_f1_score)


F1 score for training set: 1.0

In [9]:
# Predict on test data
test_f1_score, _ = predict_labels(clf, X_test, y_test)
print "F1 score for test set: {}".format(test_f1_score)


F1 score for test set: 0.713178294574

In [10]:
# Train and predict using different training set sizes
def train_predict(clf, X_train, y_train, X_test, y_test):
    clf, time_training = train_classifier(clf, X_train, y_train)
    score_training, _  = predict_labels(clf, X_train, y_train)
    score_testing, time_predicting = predict_labels(clf, X_test, y_test)
    return score_training, time_training, score_testing, time_predicting

df_DT = pd.DataFrame(columns=['Set Size', 'Training time', 'Prediction time', 
                              'F1 score for training set', 'F1 score for test set'])

X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, train_size=num_train)
for num in xrange(50, 350, 50):     
    score_training, time_training, score_testing, time_testing = train_predict(tree.DecisionTreeClassifier(),
                                                                               X_train[:num], y_train[:num],
                                                                               X_test, y_test)
    df_DT = df_DT.append({'Set Size':num,'Training time':time_training, 
                    'Prediction time': time_testing,
                    'F1 score for training set': score_training,
                    'F1 score for test set': score_testing}, ignore_index=True)

In [11]:
# Table of Decision Tree
from IPython.display import display, HTML
display(df_DT)


Set Size Training time Prediction time F1 score for training set F1 score for test set
0 50 0.001025 0.000407 1 0.769231
1 100 0.001667 0.000290 1 0.751880
2 150 0.003843 0.000475 1 0.721311
3 200 0.004811 0.000311 1 0.755906
4 250 0.002747 0.000391 1 0.752000
5 300 0.002846 0.000418 1 0.703125

SVM Model

  • Strengths:
    • Effective in high dimensional spaces.
    • It has a regularisation parameter, which makes the user think about avoiding over-fitting.
    • Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
  • Weakness:
    • The biggest limitation of the support vector approach lies in choice of the kernel.
    • A second limitation is speed and size, both in training and testing.
    • It will not work well if there are a lof noise in data.
  • Why did you choose this model to apply?
    • Because SVM could be effective in high dimensional spaces, this data set does have a lot of features.
    • Besides SVM could have better generalization than decision tree.

In [12]:
# Train a SVM model
from sklearn import svm
clf, _ = train_classifier(svm.SVC(), X_train, y_train)  # note: using entire training set here
print clf


SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

In [13]:
# Predict on training set and compute F1 score
train_f1_score, _ = predict_labels(clf, X_train, y_train)
print "F1 score for training set: {}".format(train_f1_score)


F1 score for training set: 0.865671641791

In [14]:
# Predict on test data
test_f1_score, _ = predict_labels(clf, X_test, y_test)
print "F1 score for test set: {}".format(test_f1_score)


F1 score for test set: 0.802631578947

In [15]:
df_SVC = pd.DataFrame(columns=['Set Size', 'Training time', 'Prediction time', 
                              'F1 score for training set', 'F1 score for test set'])

for num in xrange(50, 350, 50):     
    score_training, time_training, score_testing, time_testing = train_predict(svm.SVC(), 
                                                                               X_train[:num], y_train[:num],
                                                                               X_test, y_test)
    df_SVC = df_SVC.append({'Set Size':num,'Training time':time_training, 
                    'Prediction time': time_testing,
                    'F1 score for training set': score_training,
                    'F1 score for test set': score_testing}, ignore_index=True)

In [16]:
# Table of SVC
display(df_SVC)


Set Size Training time Prediction time F1 score for training set F1 score for test set
0 50 0.001414 0.000848 0.919540 0.792208
1 100 0.002251 0.001439 0.862275 0.789809
2 150 0.035185 0.001947 0.871369 0.794872
3 200 0.007054 0.001959 0.850649 0.792208
4 250 0.008978 0.003932 0.870466 0.805195
5 300 0.011502 0.002788 0.865672 0.802632

AdaBoost Model

  • Strengths:
    • It has good generalization.
    • It can achieve similar classification results with much less tweaking of parameters or settings.
    • Versatile - a wide range of base learners can be used with AdaBoost.
  • Weakness:
    • It can be sensitive to noisy data and outliers.
    • Weak learner should not be too complex to avoid overfitting.
    • There needs to be enough data so that the weak learning requirement is satisfied.
  • Why did you choose this model to apply?
    • Because we only have small number of data set and many features, the simple decision tree model could be the good choice for base estimator.
    • And AdaBoost could reach better generaliztion than Decision Tree does.

In [17]:
# Train a AdaBoost model with Decision Tree as a base estimator
from sklearn.ensemble import AdaBoostClassifier
clf,_ = train_classifier(AdaBoostClassifier(), X_train, y_train)  # note: using entire training set here
print clf


AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=1.0, n_estimators=50, random_state=None)

In [18]:
# Predict on training set and compute F1 score
train_f1_score,_ = predict_labels(clf, X_train, y_train)
print "F1 score for training set: {}".format(train_f1_score)


F1 score for training set: 0.873563218391

In [19]:
# Predict on test data
test_f1_score,_ = predict_labels(clf, X_test, y_test)
print "F1 score for test set: {}".format(test_f1_score)


F1 score for test set: 0.814814814815

In [20]:
# Train and predict using different training set sizes
df_Ada = pd.DataFrame(columns=['Set Size', 'Training time', 'Prediction time', 
                              'F1 score for training set', 'F1 score for test set'])

for num in xrange(50, 350, 50):
    score_training, time_training, score_testing, time_testing = train_predict(AdaBoostClassifier(), 
                                                                               X_train[:num], y_train[:num], 
                                                                               X_test, y_test)
    df_Ada = df_Ada.append({'Set Size':num,'Training time':time_training, 
                    'Prediction time': time_testing,
                    'F1 score for training set': score_training,
                    'F1 score for test set': score_testing}, ignore_index=True)

In [21]:
# Table of AdaBoost
display(df_Ada)


Set Size Training time Prediction time F1 score for training set F1 score for test set
0 50 0.258665 0.009880 1.000000 0.763889
1 100 0.182984 0.009308 0.979592 0.742424
2 150 0.243670 0.016290 0.940639 0.713178
3 200 0.183352 0.009192 0.851986 0.766917
4 250 0.179279 0.009119 0.847458 0.814286
5 300 0.185986 0.009215 0.873563 0.814815

Choosing the Best Model

  • Based on the experiments you performed earlier, in 1-2 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?
  • In 1-2 paragraphs explain to the board of supervisors in layman's terms how the final model chosen is supposed to work (for example if you chose a Decision Tree or Support Vector Machine, how does it make a prediction).
  • Fine-tune the model. Use Gridsearch with at least one important parameter tuned and with at least 3 settings. Use the entire training set for this.
  • What is the model's final F1 score?

Comparing Models

  • Training time: AdaBoost > SVM > Decision Tree
  • Predicting time: AdaBoost > SVM > Decision Tree
  • F1 score for test set on size of 300: AdaBoost > SVM > Decision Tree

I would like to choose Adaboost for this case, because

  1. The Fl scose of this model is the best based on the above results.
  2. The training time could be acceptable, although it's highest in these three models.
  3. Because our goal is to model the factors that predict how likely a student is to pass their high school final exam and take some intervention in advanced, we do have time to compute the result. So we should focus on the F1 score instead of computational cost.

How AdaBoost works

  • AdaBoost is a type of "Ensemble Learning" where multiple classifiers are employed to build a stronger learning algorithm. AdaBoost works by choosing a base classifier (e.g. decision trees) and iteratively improving it by accounting for the incorrectly classified examples in the training set.
  • Training :
    • First, we assign equal weights to all the training examples and choose a base classifier (e.g. decision trees). We can think of each weight as an importance weight that tells us how important the example is for our current learning task
    • Secondly, we apply the base classifier (e.g. decision trees) to the training set and increase the weights of the incorrectly classified examples. Then the next classifier will “pay more attention” to the incorrectly classified example during training, since now has a greater weight.
    • We iterate n times, each time applying base classifier on the training set with updated weights. The final model is the weighted sum of the n classifiers.
  • Predicting :
    • For n classifiers, each classifier computes their results and multiply by their own weights, then sum up.
    • Then based on the sum, deciding how to label this data.
    • In this way, classifiers that have a poor accuracy (high error rate, low weight) are penalized in the final sum.

In [22]:
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score
from sklearn.metrics import make_scorer
from sklearn.cross_validation import StratifiedShuffleSplit

param_grid = {"n_estimators": np.arange(3,20,2), "learning_rate": np.arange(0.5,0.8,0.02)}
scoring_function = make_scorer(f1_score, pos_label='yes', average='binary')

clf = GridSearchCV(AdaBoostClassifier(), param_grid=param_grid, scoring=scoring_function)
clf.fit(X_train, y_train)
training_score,_ = predict_labels(clf.best_estimator_, X_train, y_train)
testing_score,_ = predict_labels(clf.best_estimator_, X_test, y_test)
print "Best parameters: {}".format(clf.best_params_)
print "F1 score for training set: {}".format(training_score)
print "F1 score for test set: {}".format(testing_score)


Best parameters: {'n_estimators': 7, 'learning_rate': 0.64000000000000012}
F1 score for training set: 0.835820895522
F1 score for test set: 0.802721088435