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.
Answer: This is a textbook classification problem, as we are trying to classify whether or not the students need additional support. Had we been asked to compute the likelihood of students failing, and therefore needing additional support, that would have been a regression problem.
In [1]:
# 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!"
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:
n_students.n_features.n_passed.n_failed.grad_rate, in percent (%).
In [2]:
# number of students
n_students = student_data.shape[0]
# number of features (excluding labeled feature)
n_features = student_data.shape[1] - 1
# passing student count
n_passed = student_data.loc[student_data["passed"]=="yes"].shape[0]
# failing student count
n_failed = student_data.loc[student_data["passed"]=="no"].shape[0]
# graduation rate
grad_rate = float(n_passed) / n_students * 100
# 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)
In this section, we will prepare the data for modeling, training and testing.
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.
In [3]:
# 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()
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.
In [4]:
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))
In [5]:
X_all.head()
Out[5]:
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:
X_all, y_all) into training and testing subsets.random_state for the function(s) you use, if provided.X_train, X_test, y_train, and y_test.
In [6]:
# Load train_test_split library
from sklearn.cross_validation import train_test_split
# Set the number of training points
num_train = 300
# Set the number of testing points
num_test = X_all.shape[0] - num_train
# Shuffle and split the dataset into the number of training and testing points above
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=0)
# 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])
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.
The following supervised learning models are currently available in scikit-learn that you may choose from:
List three supervised learning models that are appropriate for this problem. For each model chosen
Answer:
Given some the cursory research performed previously, we've selected support vector machines, ensemble methods (of some type), and Gaussian naive Bayes classifiers. Answers to the above question are below.
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:
train_classifier - 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 performs train_clasifier and predict_labels.
In [7]:
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))
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:
clf_A, clf_B, and clf_C.random_state for each model you use, if provided.X_train and y_train.
In [8]:
# Import the three supervised learning models from sklearn
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
# Initialize the three models
seed = 1
clf_A = svm.SVC(random_state=seed)
clf_B = RandomForestClassifier(random_state=seed)
clf_C = GaussianNB()
# Train and evaluate classifiers
for size in [100, 200, 300]:
print("\n")
train_predict(clf_A, X_train.iloc[0:size], y_train.iloc[0:size], X_test, y_test)
train_predict(clf_B, X_train.iloc[0:size], y_train.iloc[0:size], X_test, y_test)
train_predict(clf_C, X_train.iloc[0:size], y_train.iloc[0:size], X_test, y_test)
Edit the cell below to see how a table can be designed in Markdown. You can record your results from above in the tables provided.
Classifer 1 - Support Vector Machine
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0011 | 0.0008 | 0.8591 | 0.7838 |
| 200 | 0.0028 | 0.0011 | 0.8693 | 0.7755 |
| 300 | 0.0052 | 0.0024 | 0.8692 | 0.7586 |
Classifer 2 - Random Forest
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0219 | 0.0007 | 0.9844 | 0.7669 |
| 200 | 0.0231 | 0.0007 | 0.9924 | 0.7153 |
| 300 | 0.0201 | 0.0007 | 0.9976 | 0.7727 |
Classifer 3 - Gaussian Naive Bayes
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0005 | 0.0002 | 0.8550 | 0.7481 |
| 200 | 0.0006 | 0.0002 | 0.8321 | 0.7132 |
| 300 | 0.0007 | 0.0002 | 0.8088 | 0.7500 |
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.
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: Based on available data, limited resources, cost, and performance, the Random Forest (RF) method appears to be the most appropriate. While it relatively more costly to train, by approximately 10x ~ 100x over the other two methods, at ~0.02s, for any training set size, this disadvantage is of little concern (especially considering that it remains roughly constant, regardless of training dataset size). Also, prediction time was blazing fast at ~0.0007s. Although all three classifiers demonstrated F1 accuracy near 75%, RF had F1 train scores of nearly unity. The larger disparity between F1 train and test scores seen for the RF method possibly suggests that it was overfitting the data and can be tuned to decrease such high variance.
Although beyond the scope of this project, the RF method can also identify the features most strongly associated with students' failure, which may allow school administrators to focus on proactive measures as well. Moreover, RFs are easier to explain to the general public, as decision trees, which are central to RFs, are quite intuitive.
In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.
Answer: We have been able to leverage past student data, including attributes (e.g., gender, age, familial size, alcohol consumption) and performance (i.e., pass/fail final exam), to develop an algorithm that is able to classify future student failure/pass behavior for the high school final exam.
Using 300 previous observations, i.e., data from 300 students, including both attributes and performance, we were able to create a random forest model -- which sounds complicated, but isn't! First, it is helpful to understand that a random forest model is composed of decision trees -- the same kind of decision trees that your brain is creating and invoking every moment of each day. For example, when you start your day and decide if you should grab a coat, your mind may first ask: will the temperature remain above 72F? If yes, then secondly it may ask, is rain in the forecast? Maybe, if it's above 72F and sunny, then your brain will forego the coat. However, had it been less than 72F or rainy, then a coat would have accompanied your day. This process of leveraging two features, e.g., temperature, running a logical evaluation process, e.g., is it greater than 72F, and returning a result, e.g., grab a coat, is just one example of how our brains use decision trees daily.
A random forest model employs a similar approach, first creating a tree-at-a-time. In general, to create a tree from these variables, it attempts to find features (sunny/rainy, temperature) that divides the population into roughly equivalent samples. Essentially, it uses a divide and conquer approach, so if previous observations were all sunny, but had a good mix of temperature ranges, it would first use temperature to subdivide the population into smaller groups. This process of tree building is generally continued until the the tree's leafs reach desireablly small groups, or until all features are used. When the tree is complete, the leafs that contain observations are labelled according to the majority label (pass or fail). For example, if a leaf at the end of a tree contains 10 students that have passed, but 3 that have failed, it will label this "bucket" as "pass", such that when a future prediction occurs for another student that leads them into the same bucket, the model will return predict a "pass" as well.
However, the problem creating a single tree is that it could have built a model that was too specific to the training observations (we call this overfitting). To help the model generalize such that it'll perform well on future unseen data, the random forest method builds many trees and "averages" the results to better generalize to future data.
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:
sklearn.grid_search.GridSearchCV and sklearn.metrics.make_scorer.parameters = {'parameter' : [list of values]}.clf.make_scorer and store it in f1_scorer.pos_label parameter to the correct value!clf using f1_scorer as the scoring method, and store it in grid_obj.X_train, y_train), and store it in grid_obj.
In [9]:
# Import 'GridSearchCV' and 'make_scorer'
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import make_scorer
# Create the parameters list you wish to tune
parameters = [{
"criterion": ["gini","entropy"],
"max_depth": np.arange(4,8,2),
"max_features": np.arange(5,9,2),
"n_estimators": np.arange(11,15,2)
}]
# Initialize the classifier
seed = 1
clf = RandomForestClassifier(random_state=seed, n_jobs=1)
# Make an f1 scoring function using 'make_scorer'
f1_scorer = make_scorer(f1_score, pos_label="yes")
# Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(clf, 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 grid_obj.best_params_
print grid_obj.best_score_
# 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))
Answer: A handful of numerical experiments demonstrated some rather interesting results.
Four variables were considered: criterion, max_depth, max_features, and n_estimators. An iterative mutltigrid-like approach was employed, first beginning with large coarse grids, and slowly switching to smaller, finer grids, once appropriate ranges for each variable were empirically determined. This was done to lessen the overall compuational burden of the grid search method.
Using step sizes of 1, the following parametrized values were found to be optimal, according to the grid search: {'max_features': 6, 'n_estimators': 11, 'criterion': 'gini', 'max_depth': 6} This resulted in a grid search score of 0.83, but when these parameters were used to create the 'tuned' model, the F1 training and test scores were 0.9095 and 0.7639. Unexpectedly, the test score was slightly less than the 'untuned' model test score.
Inquisitive, upon reverting back to step sizes of 2, the following parametrized values were found to be optimal: {'max_features': 7, 'n_estimators': 13, 'criterion': 'gini', 'max_depth': 6} Here, the difference being that _maxfeatures was bumped from 6 to 7. As expected, reducing the resolution obtainable by the grid search, the resulting score also reduced -- slightly from 0.83 to 0.8229. However, by under optimizing the grid search result, these new parameters created 'tuned' model F1 train and test scores of 0.9031 and 0.8058.
Fascinating!
There are two things likely at play here. First, is that the grid search metric for optimal parameters and the test set metric are dissimilar. The grid search metric is using a k-fold cross validation style on training data, while the test metric is computing an F1 score for a test set. Since the populations and methodologies are different, we'd not to surprised by these dissimilar results. Additionally, the small sample size is likely also reducing the model's ability to generalize. Perhaps, had more observations been available, these differences would become less significant.
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.