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.
In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.
The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.
Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, 'income', will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database.
In [2]:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualization code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Census dataset
data = pd.read_csv("census.csv")
# Success - Display the first record
display(data.head(n=1))
A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:
'n_records''n_greater_50k'.'n_at_most_50k'.'greater_percent'.Hint: You may need to look at the table above to understand how the 'income' entries are formatted.
In [3]:
# TODO: Total number of records
n_records = data['age'].count()
# TODO: Number of records where individual's income is more than $50,000
n_greater_50k = data[data.income==">50K"].income.count()
#df[df.a > 1].sum()
#data[data['income']==">50K"].count()
# TODO: Number of records where individual's income is at most $50,000
n_at_most_50k = data[data.income=="<=50K"].income.count()
#data[data['income']=="<=50K"].count()
# TODO: Percentage of individuals whose income is more than $50,000
greater_percent = float(n_greater_50k)*100/n_records
# Print the results
print "Total number of records: {}".format(n_records)
print "Individuals making more than $50,000: {}".format(n_greater_50k)
print "Individuals making at most $50,000: {}".format(n_at_most_50k)
print "Percentage of individuals making more than $50,000: {:.2f}%".format(greater_percent)
Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as preprocessing. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms.
A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number. Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: 'capital-gain' and 'capital-loss'.
Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed.
In [4]:
# Split the data into features and target label
income_raw = data['income']
features_raw = data.drop('income', axis = 1)
# Visualize skewed continuous features of original data
vs.distribution(data)
For highly-skewed feature distributions such as 'capital-gain' and 'capital-loss', it is common practice to apply a logarithmic transformation on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of 0 is undefined, so we must translate the values by a small amount above 0 to apply the the logarithm successfully.
Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed.
In [5]:
# Log-transform the skewed features
skewed = ['capital-gain', 'capital-loss']
features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))
# Visualize the new log distributions
vs.distribution(features_raw, transformed = True)
In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as 'capital-gain' or 'capital-loss' above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.
Run the code cell below to normalize each numerical feature. We will use sklearn.preprocessing.MinMaxScaler for this.
In [6]:
# Import sklearn.preprocessing.StandardScaler
from sklearn.preprocessing import MinMaxScaler
# Initialize a scaler, then apply it to the features
scaler = MinMaxScaler()
numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
features_raw[numerical] = scaler.fit_transform(data[numerical])
# Show an example of a record with scaling applied
display(features_raw.head(n = 1))
From the table in Exploring the Data above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called categorical variables) be converted. One popular way to convert categorical variables is by using the one-hot encoding scheme. One-hot encoding creates a "dummy" variable for each possible category of each non-numeric feature. For example, assume someFeature has three possible entries: A, B, or C. We then encode this feature into someFeature_A, someFeature_B and someFeature_C.
| someFeature | someFeature_A | someFeature_B | someFeature_C | ||
|---|---|---|---|---|---|
| 0 | B | 0 | 1 | 0 | |
| 1 | C | ----> one-hot encode ----> | 0 | 0 | 1 |
| 2 | A | 1 | 0 | 0 |
Additionally, as with the non-numeric features, we need to convert the non-numeric target label, 'income' to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("<=50K" and ">50K"), we can avoid using one-hot encoding and simply encode these two categories as 0 and 1, respectively. In code cell below, you will need to implement the following:
pandas.get_dummies() to perform one-hot encoding on the 'features_raw' data.'income_raw' to numerical entries.0 and records with ">50K" to 1.
In [14]:
# TODO: One-hot encode the 'features_raw' data using pandas.get_dummies()
features = pd.get_dummies(features_raw)
# TODO: Encode the 'income_raw' data to numerical values
income = income_raw.apply(lambda x: 1 if x == ">50K" else 0)
# Print the number of features after one-hot encoding
encoded = list(features.columns)
print "{} total features after one-hot encoding.".format(len(encoded))
# Uncomment the following line to see the encoded feature names
#print encoded
Now all categorical variables have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.
Run the code cell below to perform this split.
In [8]:
# Import train_test_split
from sklearn.cross_validation import train_test_split
# Split the 'features' and 'income' data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, 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])
CharityML, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using accuracy as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that does not make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is more important than the model's ability to recall those individuals. We can use F-beta score as a metric that considers both precision and recall:
$$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the F$_{0.5}$ score (or F-score for simplicity).
Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect accuracy, since we could simply say "this person does not make more than \$50,000" and generally be right, without ever looking at the data! Making such a statement would be called naive, since we have not considered any information to substantiate the claim. It is always important to consider the naive prediction for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, CharityML would identify no one as donors.
In [12]:
# TODO: Calculate accuracy
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import fbeta_score
income_pred=income.apply(lambda x:1)
TP=sum(map(lambda x,y: 1 if x==1 and y==1 else 0, income,income_pred)) #True Pos
FP=sum(map(lambda x,y: 1 if x==0 and y==1 else 0, income,income_pred)) #False Pos
FN=sum(map(lambda x,y: 1 if x==1 and y==0 else 0, income,income_pred)) #False Neg
# accuracy = TP/(TP+FP)
accuracy = float(TP)/(TP+FP)
# The commented code below was used to confirm the precision calculation was correct
#accuracy1 = accuracy_score(income,income_pred)
#print 'accuracy comparison',accuracy,accuracy1
# recall = TP/(TP+FN)
recall=float(TP)/(TP+FN)
# The commented code below was used to confirm the recall calculation was correct
#recal1=recall_score(income,income_pred)
#print 'recall comparison',recal1,recall1
# TODO: Calculate F-score using the formula above for beta = 0.5
beta=0.5
fscore = (1+beta**2)*(accuracy*recall)/(beta**2*accuracy+recall)
#fscore1=fbeta_score(income,income_pred, beta=0.5)
#print 'fscore comparison',fscore,fscore1
# Print the results
print "Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]".format(accuracy, fscore)
The following supervised learning models are currently available in scikit-learn that you may choose from:
List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen
Answer: I believe the most appropriate models to solve this problem are Gaussian Naive Bayes, Support Vector Machines (SVC) and the ensemble method Adaboost.
The Gaussian Naive Bayes model has been widely used in the industry for text classification purposes. In particular for classifying and filtering spam emails based on the likelihood of certain words appearing on an spam email as compared to a non-spam email (ref: https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering). The strenght of this model lies in its simplicity inherited from the assumption that all features needed correctly to classify the data are "independent" from each other. This allows for the algorithm to be trained quickly and using less data. Furthermore, even when the underlying independence assumption is broken (i.e. when features have relationships between them), this algorithm still performs very well and is able to classify based on the maximum likelihood of a label given the set of features rather than on the actual probabilities of each feature. On the other hand, Naive Bayes' weakness is that if it encounters a previously unseen feature-label combination (on which it was never trained) it will incorrectly estimate likelyhood as 0 which can cause it to incorrectly classify the label. This particular model could be a good approach to solving the problem as we have a large data set with few features.
Support Vector Machines (SVC) have been used in the industry successfully for handwritten digit identification to a level close to human classification error (ref: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.951&rep=rep1&type=pdfProne). This model is effective in high dimensional spaces and best suited for problems with complex domains where there are clear margins of separation in the data. To correctly classify the data, this model is also capable of defining complex higher order separation planes through custom kernels. One of the weaknesses of this model is that training can be time consuming on large datasets. Also, SVCs can perform poorly and overfit when when the data is very noisy or overlaps too much. While training on our large current dataset may become an issue, the model could be still be a good candidate as there seems to be some features in the data that can more clearly define the income level boundary.
Adaboost is a Boosting type Ensemble Learning Method. In the industry, boosting algorithms have been used for the binary classification problem of face detection where the algorithm has to identify wheter a portion of an image is a face or background (ref: https://en.wikipedia.org/wiki/Boosting_(machine_learning)). One of the main strenghts of Adaboost is that it is a fast algorithm, agnostic to the classifier and less prone to overfitting. During the iterative training, it continuously gives more weight to missclassified labels to allow the classifier to focus on the harder cases which increases the overall model's performance. On the other hand, noisy data and outliers in the data can negatively impact the performance so data pre processing is important. Furthermore, if a complex model is used as the base classifier, this can lead to overfitting to the training data. In my opinion, this model is a good candidate for the problem as our dataset is large yet clean. Therefore we will be able to perform multiple quick trainining iterations to maximize our overall accuracy on the unseen testing data.
To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section. In the code block below, you will need to implement the following:
fbeta_score and accuracy_score from sklearn.metrics.X_test, and also on the first 300 training points X_train[:300].beta parameter!
In [10]:
# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score
from sklearn.metrics import fbeta_score, accuracy_score
def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):
'''
inputs:
- learner: the learning algorithm to be trained and predicted on
- sample_size: the size of samples (number) to be drawn from training set
- X_train: features training set
- y_train: income training set
- X_test: features testing set
- y_test: income testing set
'''
results = {}
# TODO: Fit the learner to the training data using slicing with 'sample_size'
start = time() # Get start time
learner.fit(X_train[:sample_size],y_train[:sample_size])
end = time() # Get end time
# TODO: Calculate the training time
results['train_time'] = end-start
# TODO: Get the predictions on the test set,
# then get predictions on the first 300 training samples
start = time() # Get start time
predictions_test = learner.predict(X_test)
predictions_train = learner.predict(X_train[:300])
end = time() # Get end time
# TODO: Calculate the total prediction time
results['pred_time'] = end-start
# TODO: Compute accuracy on the first 300 training samples
results['acc_train'] = accuracy_score(y_train[:300],predictions_train)
# TODO: Compute accuracy on test set
results['acc_test'] = accuracy_score(y_test,predictions_test)
# TODO: Compute F-score on the the first 300 training samples
results['f_train'] = fbeta_score(y_train[:300],predictions_train,beta=0.5)
# TODO: Compute F-score on the test set
results['f_test'] = fbeta_score(y_test,predictions_test,beta=0.5)
# Success
print "{} trained on {} samples.".format(learner.__class__.__name__, sample_size)
# Return the results
return results
In the 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.'samples_1', 'samples_10', and 'samples_100' respectively.Note: Depending on which algorithms you chose, the following implementation may take some time to run!
In [11]:
# TODO: Import the three supervised learning models from sklearn
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier
# TODO: Initialize the three models
clf_A = GaussianNB()
clf_B = SVC(random_state=0)
clf_C = AdaBoostClassifier(random_state=0)
# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data
samples_1 = len(X_train)/100
samples_10 = len(X_train)/10
samples_100 = len(X_train)
# Collect results on the learners
results = {}
for clf in [clf_A, clf_B, clf_C]:
clf_name = clf.__class__.__name__
results[clf_name] = {}
for i, samples in enumerate([samples_1, samples_10, samples_100]):
results[clf_name][i] = \
train_predict(clf, samples, X_train, y_train, X_test, y_test)
# Run metrics visualization for the three supervised learning models chosen
vs.evaluate(results, accuracy, fscore)
Based on the evaluation you performed earlier, in one to two paragraphs, explain to CharityML which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000.
Hint: Your answer should include discussion of the metrics, prediction/training time, and the algorithm's suitability for the data.
Answer: In my opinion, the most appropriate solution to this problem would be achieved using the Adaboost ensemble model. As it can be observed from the plots, in comparison to SVC, both the Adaboost and the GaussianNB algorithms are capable of both training on the entire data set and quickly performing predictions. However, at all training set sizes, the training and testing accuracies achieved with Adaboost are significantly higher than those obtained when using the simpler GuassianNB algorithm and as high as those ahieved by the more complex SVC model. Also, In terms of the Fbeta score, Adaboost slightly outperforms SVC at all training set sizes and significately outperforms GaussianNB.
When weighing all of these attributes, we can conclude that Adaboost is best suited for this problem. We will be able to fully utilize the entire dataset to maximize the performance in the unseen data without compromising on training and testing the speeds. Also, since we are more interested in maximizing the number of accurately classified people that make more than 50K, this model, which has the highest accuracy, seems to be the most appropriate.
In one to two paragraphs, explain to CharityML, 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:
Adaboost is a boosting type ensemble learner. This method works by combining multiple individual "weak" learning hypotheses to create one strong model. Each weak hypothesis used is better at classifying the data than random chance. However, it's the combination of all of these independent weak learning hypotheses what makes the model more capable of predicting accurately on unseen data than each of the individual hypothesis would.
This algorithm is trained iteratively. During each training iteration, the model attempts to correctly classify the training data. Based on how well it performed during the iteration, the algorithm assigns higher weights to the observations that it was unable to classify correctly. On the next training iteration, the algorithm focuses on those more complex, initially missclassified observations, and tries to classify them correctly. Again based on the results of this training iteration Adaboost assigns higher weights to those observations that were missclassified. This process is repeated for a number of iterations defined by the user in an attempt to find the set of weighted hypotheses that combined will perform best on unseen data.
Finally, to make predictions, Adaboost uses the weak independent hypotheses on which it was trained during each iteration and developsindividual predictions that are then weighted accordingly to come up with the most probable single strong prediction.
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.clf.random_state if one is available to the same state you set before.parameters = {'parameter' : [list of values]}.max_features parameter of your learner if that parameter is available!make_scorer to create an fbeta_score scoring object (with $\beta = 0.5$).clf using the 'scorer', and store it in grid_obj.X_train, y_train), and store it in grid_fit.Note: Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!
In [14]:
# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import fbeta_score, make_scorer
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
# TODO: Initialize the classifier
clf = AdaBoostClassifier(random_state=0)
# TODO: Create the parameters list you wish to tune
#parameters = {'n_estimators':[75,100,200]}
parameters = {'n_estimators':[75,200,500],'learning_rate':[1.0,1.5,2.0]}
# TODO: Make an fbeta_score scoring object
scorer = make_scorer(fbeta_score, beta=0.5)
# TODO: Perform grid search on the classifier using 'scorer' as the scoring method
grid_obj = GridSearchCV(clf, parameters,scoring=scorer)
# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_fit = grid_obj.fit(X_train, y_train)
# Get the estimator
best_clf = grid_fit.best_estimator_
# Make predictions using the unoptimized and model
predictions = (clf.fit(X_train, y_train)).predict(X_test)
best_predictions = best_clf.predict(X_test)
# Report the before-and-afterscores
print "Unoptimized model\n------"
print "Accuracy score on testing data: {:.4f}".format(accuracy_score(y_test, predictions))
print "F-score on testing data: {:.4f}".format(fbeta_score(y_test, predictions, beta = 0.5))
print "\nOptimized Model\n------"
print "Final accuracy score on the testing data: {:.4f}".format(accuracy_score(y_test, best_predictions))
print "Final F-score on the testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5))
What is your optimized model's accuracy and F-score on the testing data? Are these scores better or worse than the unoptimized model? How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in Question 1?
Note: Fill in the table below with your results, and then provide discussion in the Answer box.
Answer: From the scores, it can be seen that both the unoptimized and optimized models perform significantly better than the naive predictor benchmarks. When comparing between the optmized and unoptimized models, the optimized model performs slightly better.
An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.
Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a feature_importance_ attribute, which is a function that ranks the importance of features according to the chosen classifier. In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset.
When Exploring the Data, it was shown there are thirteen available features for each individual on record in the census data.
Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?
Answer: In my opinion, the features that are most important for prediction and my reasoning are ranked and explained below:
1- Age: Younger people are usually less experienced than older people and therefore also paid less.
2- Hours per week: Assuming that two people have the same hourly rate, the one that works more hours has the potential of earning more money and is more likely to be making more than 50K.
3- Education level: People with higher education also have better chances of finding higher paying jobs
4- Workclass: Working for the state or government does not always pay as much as working in the private sector.
5- Native country:People in first world countries, such as USA tend to earn more income that in other countries.
Choose a scikit-learn supervised learning algorithm that has a feature_importance_ attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.
In the code cell below, you will need to implement the following:
'.feature_importances_'.
In [15]:
# TODO: Import a supervised learning model that has 'feature_importances_'
# TODO: Train the supervised model on the training set
#AdaBoostClassifier(random_state=0)
model = AdaBoostClassifier(random_state=0,n_estimators=500).fit(X_train, y_train)
# TODO: Extract the feature importances
importances = model.feature_importances_
# Plot
vs.feature_plot(importances, X_train, y_train)
Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.
How do these five features compare to the five features you discussed in Question 6? If you were close to the same answer, how does this visualization confirm your thoughts? If you were not close, why do you think these features are more relevant?
Answer:My predictions were only partially right. However, I was not expecting capital gains and capital losses to play an important role. Nevertheless, this algorithm gives these two features higher weights as their data probably contains better indicators of income level of the people which aids the algorithm in defining a stronger and more accurate decision boundary. On the other hand, I did expected Age, hours-per-week and education number to be part of the driving predictive features due to the reasons mentioned in my answer to the question above.
How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of all features present in the data. This hints that we can attempt to reduce the feature space and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set with only the top five important features.
In [16]:
# Import functionality for cloning a model
from sklearn.base import clone
# Reduce the feature space
X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]
X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]
# Train on the "best" model found from grid search earlier
clf = (clone(best_clf)).fit(X_train_reduced, y_train)
# Make new predictions
reduced_predictions = clf.predict(X_test_reduced)
# Report scores from the final model using both versions of data
print "Final Model trained on full data\n------"
print "Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, best_predictions))
print "F-score on testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5))
print "\nFinal Model trained on reduced data\n------"
print "Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, reduced_predictions))
print "F-score on testing data: {:.4f}".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))
Answer: Reducing the data to only five features also reduces the F-score and accuracy of the predictions. I believe this is due to the classifier not being able to accurately capture the complexity of the data and therefore not being able to properly generalize. However, if the training time was an important factor, reducing the number of features should reduce training time. Furthermore, since the impact on the accuracy and/or the F-scores is not too large, the small increase in classification error could be an appropriate tradeoff in that scenario.
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