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: Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. 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.
# 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
# disable warnings
import warnings
warnings.filterwarnings("ignore")
# 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))
age | workclass | education_level | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | income | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 39 | State-gov | Bachelors | 13.0 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174.0 | 0.0 | 40.0 | United-States | <=50K |
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.
# TODO: Total number of records
n_records = len(data)
# TODO: Number of records where individual's income is more than $50,000
n_greater_50k = len(data[data['income'] == '>50K'])
# TODO: Number of records where individual's income is at most $50,000
n_at_most_50k = len(data[data['income'] == '<=50K'])
# TODO: Percentage of individuals whose income is more than $50,000
greater_percent = n_greater_50k/(n_greater_50k + n_at_most_50k)*100
# 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: {}%".format(greater_percent))
Total number of records: 45222 Individuals making more than $50,000: 11208 Individuals making at most $50,000: 34014 Percentage of individuals making more than $50,000: 24.78439697492371%
** Featureset Exploration **
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.
# 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.
# Log-transform the skewed features
skewed = ['capital-gain', 'capital-loss']
features_log_transformed = pd.DataFrame(data = features_raw)
features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))
# Visualize the new log distributions
vs.distribution(features_log_transformed, 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.
# Import sklearn.preprocessing.StandardScaler
from sklearn.preprocessing import MinMaxScaler
# Initialize a scaler, then apply it to the features
scaler = MinMaxScaler() # default=(0, 1)
numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)
features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])
# Show an example of a record with scaling applied
display(features_log_minmax_transform.head(n = 5))
age | workclass | education_level | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.301370 | State-gov | Bachelors | 0.800000 | Never-married | Adm-clerical | Not-in-family | White | Male | 0.667492 | 0.0 | 0.397959 | United-States |
1 | 0.452055 | Self-emp-not-inc | Bachelors | 0.800000 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0.000000 | 0.0 | 0.122449 | United-States |
2 | 0.287671 | Private | HS-grad | 0.533333 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0.000000 | 0.0 | 0.397959 | United-States |
3 | 0.493151 | Private | 11th | 0.400000 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0.000000 | 0.0 | 0.397959 | United-States |
4 | 0.150685 | Private | Bachelors | 0.800000 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0.000000 | 0.0 | 0.397959 | Cuba |
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_log_minmax_transform'
data.'income_raw'
to numerical entries.0
and records with ">50K" to 1
.# TODO: One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()
features_final = pd.get_dummies(features_log_minmax_transform)
# TODO: Encode the 'income_raw' data to numerical values
income = income_raw.map({'<=50K':0, '>50K': 1})
# Print the number of features after one-hot encoding
encoded = list(features_final.columns)
print("{} total features after one-hot encoding.".format(len(encoded)))
# Uncomment the following line to see the encoded feature names
#print (encoded)
103 total features after one-hot encoding.
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.
# Import train_test_split
from sklearn.model_selection 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_final,
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]))
Training set has 36177 samples. Testing set has 9045 samples.
In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a naive predictor.
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.
** Accuracy ** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).
** Precision ** tells us what proportion of messages we classified as spam, actually were spam. It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of
[True Positives/(True Positives + False Positives)]
** Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam. It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of
[True Positives/(True Positives + False Negatives)]
For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios).
'accuracy'
and 'fscore'
to be used later.** Please note ** that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.
** HINT: **
'''
TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data
encoded to numerical values done in the data preprocessing step.
FP = income.count() - TP # Specific to the naive case
TN = 0 # No predicted negatives in the naive case
FN = 0 # No predicted negatives in the naive case
'''
# TODO: Calculate accuracy, precision and recall
# Start from the 4 elements from the confusion matrix
TP = n_greater_50k
FP = n_records - n_greater_50k
TN = 0
FN = 0
# Now find accuracy/precision/recall
accuracy = (TP + TN) / n_records
recall = TP / (TP + FN)
precision = TP / (TP + FP)
# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.
beta = 0.5
fscore = (1 + beta**2)*((precision * recall)/((beta**2 * precision) + recall))
# Print the results
print("Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]".format(accuracy, fscore))
Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]
The following are some of the supervised learning models that 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
** HINT: **
Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer.
Answer:
The 3 supervised learning models that could be used to solve this problem are Adaboost, Random Forest , and Support Vector Machines
*** Describe one real-world application in industry where the model can be applied.***
Adaboost could be used as fraud detection in banking systems. Since the fraudulent transactions represent a small portion of the entire transaction space, ensemble methods could properly handle the imbalanced class issues presented in these problems
*What are the strengths of the model; when does it perform well?*
Adaboost models tends to the best accurate predictions resulted from combining strength of different weak learners. When the hyperparameters are properly tuned, the training data is properly scaled and less skewed, Adaboost models tends to perform really well. When the underlying relationship between the predictors and response is highly non-linear, Adaboost will have far superior performances than the linear classification models
*What are the weaknesses of the model; when does it perform poorly?*
Adaboost model is not very robust to outliers.Therefore, when the data distribution has heavy tails, the algorithm tends to perform poorly. Also, the adaboost model does not perform very well without proper hyperparameter tuning, sometimes can easily overfit the data
*What makes this model a good candidate for the problem, given what you know about the data?*
Our donars dataset has a large amount of categorical features that could possibly makes the model non-linear, which makes Adaboost a great candidate to work with. Our previous data preprocessing has ensured the skewed features has been properly normalized. By also removing the outliers from the dataset, it is reasonable to anticipate that our Adaboost model will perform really well and produce superior prediction performances
*** Describe one real-world application in industry where the model can be applied***
Random Forest models real-world applications should be very similar to the ones for Adaboost. One example could be crime rate predictions for a city given police department data for historical criminal records characteristics
What are the strengths of the model; when does it perform well?
Random Forest is robust to outliers, sparsity of data, and works well even with very little hyperparameters tuning. Like Adaboost, when the underlying relationship is highly non-linear, and the features are a mixture of categorical and numerical values, random forest models will perform significantly better than linear models
*What are the weaknesses of the model; when does it perform poorly?*
Random Forest builds complicated trees, which makes model's interpretability very low. Also, the computational time for random forest will also significantly increase when the model complexity increases
What makes this model a good candidate for the problem, given what you know about the data?
Similiar reasoning as Adaboost. Due to the non-linearity existed in our dataset, Random Forest will likely have a superior performance than linear models. Random Forest is really robust to different kinds of distributions, therefore would perform well with very little hyperparameter tuning
*** Describe one real-world application in industry where the model can be applied***
Support Vector Machines could be used to build sentiment analysis applications for online reviews such as movies and restaurants. Support Vector Machines are also sometimes used as the face recognition and hand reognition problems
*What are the strengths of the model; when does it perform well?*
Support Vector Machines allows us to customize the kernel when we have prior knowledge of the distribution of the features in our problem. With polynomial and radial basis function kernels, we are able to produce highly non-linear boundaries that will fit the data really well. Under binary classification settings, support vector machines provides robust and accurate predictions even when the data is sparse
*What are the weaknesses of the model; when does it perform poorly?*
Support Vector Machines algorithm does not perform well on data that has multiple classes. Support Vector Machine are also quite inefficient on datasets that have a large amount of samples. Support Vector Machines will make a n x n matrix in the intermediary step, which will significantly stall the efficiency of the algorithm
*What makes this model a good candidate for the problem, given what you know about the data?*
Our problem is a binary classification problem, which natrually will put Support Vector Machine algorithm a good candiate. The dataset does not have a huge amount of samples, which could ensure reasonable computation time for SVM to train. Also, the large number of categorical values will increase the sparsity of the data after one-hot encoding is performed. SVM will produce better results on the dataset
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!# 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' using .fit(training_features[:], training_labels[:])
start = time() # Get start time
learner = 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(X_test),
# then get predictions on the first 300 training samples(X_train) using .predict()
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 which is y_train[:300]
results['acc_train'] = accuracy_score(y_train[:300], predictions_train)
# TODO: Compute accuracy on test set using accuracy_score()
results['acc_test'] = accuracy_score(y_test, predictions_test)
# TODO: Compute F-score on the the first 300 training samples using fbeta_score()
results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta = 0.5)
# TODO: Compute F-score on the test set which is y_test
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!
# TODO: Import the three supervised learning models from sklearn
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
# TODO: Initialize the three models
clf_A = AdaBoostClassifier(random_state = 7)
clf_B = RandomForestClassifier(random_state = 7)
clf_C = SVC(random_state = 7)
# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data
# HINT: samples_100 is the entire training set i.e. len(y_train)
# HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)
# HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)
samples_100 = len(y_train)
samples_10 = round(len(y_train)*0.1)
samples_1 = round(len(y_train)*0.01)
# 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)
AdaBoostClassifier trained on 362 samples. AdaBoostClassifier trained on 3618 samples. AdaBoostClassifier trained on 36177 samples. RandomForestClassifier trained on 362 samples. RandomForestClassifier trained on 3618 samples. RandomForestClassifier trained on 36177 samples. SVC trained on 362 samples. SVC trained on 3618 samples. SVC trained on 36177 samples.
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 F-score.
** HINT: **
Look at the graph at the bottom left from the cell above(the visualization created by vs.evaluate(results, accuracy, fscore)
) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:
Answer:
From the visualization panel generated above, it is evident that AdaBoost is the best model to use for this project. In particular,
F Score - Adaboost is clearly the winner on the F Score Metric. Adaboost models consistently displayed superior performance on the test set regardless being trained on 1%, 10% or the full training set
Training/prediction Time - Random Forest and Adaboost models both produced efficient training/prediction processes evident from the shorter training time compared to Support Vector Machines
Suitabilities for the data - Both Support Vector Machines and Adaboost are suitable for the data, as there is no clear evidences for these two models overfitting the data. Random Forest is clearly overfitting the data, evident from the superior performance on the training set and subpar performance on the test set
According to the above analysis, Adaboost is the best model to use for us to solve this problem
** HINT: **
When explaining your model, if using external resources please include all citations.
Answer:
The final chosen model, Adaboost, is a model that combines all the strength of simple models together to produce superior performances. Specifically, we start with a simple classifer like decision trees, train it the classifer on our data. There will be misclassified samples made by this simple classfier, we will use a new classifier to improve the performance on these misclassified points by increasing the weights on them. We iterate that process for n times, each time we will use a new simple classfier on the updated weights of the sample. The final classification boundary will be a weighted sum of all of the simple classifiers' predictions.
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!
# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
# TODO: Initialize the classifier
clf = AdaBoostClassifier(random_state = 7)
# TODO: Create the parameters list you wish to tune, using a dictionary if needed.
# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}
parameters = {'n_estimators': [100, 150, 200, 250],
'learning_rate': [0.1, 0.5, 1, 10],
}
# TODO: Make an fbeta_score scoring object using make_scorer()
scorer = make_scorer(fbeta_score, beta = 0.5)
# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()
grid_obj = GridSearchCV(clf, parameters, scorer)
# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()
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)
# make prediction probabilities
best_predict_prob = best_clf.predict_proba(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)))
Unoptimized model ------ Accuracy score on testing data: 0.8576 F-score on testing data: 0.7246 Optimized Model ------ Final accuracy score on the testing data: 0.8653 Final F-score on the testing data: 0.7403
best_predict_prob
array([[ 0.50287054, 0.49712946], [ 0.50130097, 0.49869903], [ 0.50178991, 0.49821009], ..., [ 0.50555285, 0.49444715], [ 0.50430889, 0.49569111], [ 0.49536948, 0.50463052]])
Note: Fill in the table below with your results, and then provide discussion in the Answer box.
Metric | Unoptimized Model | Optimized Model |
---|---|---|
Accuracy Score | 0.8576 | 0.8653 |
F-score | 0.7246 | 0.7403 |
Answer:
The optimized model's accuracy 0.8653, F-score 0.7403, which are higher than the unoptimzed models. Conpared to the naive model that always predicts individual makes more than 50K, both the accuracy and the F1-Score are significantly better (Naive model Accuracy score: 0.2478, F-score: 0.2917). This is due to the fact that our positive label 1 (individuals making more than 50K) is the minority class. If we reverse the positive label to indivdiduals who makes at most 50K, we will have a much higher accuracy, but the F1 score will roughly be the same
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:
By exploring the data, I believe the five most important features from 1 to 5 is,
I believe the individual's occupation will be the most direct feature that affect an individual's income. Having a high salary paying job will increase the probability that individual's earning being greater than 50K.
Age is also a very important factor, as people tends to have a higher salary with higher age and more contributions to the society.
Other factors such as education/martital status willalso affect the individual's earnings. Education should have a weak relationship with an individual's earnings. Marital status should also be a weak predictor.
If an individual has a high capital gain from either winning a lottery or selling his/her properties, it will also increase the possibilities of this person earning more than 50K this year.
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_'
.# TODO: Import a supervised learning model that has 'feature_importances_'
from sklearn.tree import DecisionTreeClassifier
# TODO: Train the supervised model on the training set using .fit(X_train, y_train)
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# TODO: Extract the feature importances using .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.
Answer:
The five features returned by random forest are not exactly the same as the ones I expected in Question 6. I correctly predicted agem marital status and capital gains being in top 5 important features. Education_level feature has been replaced by the numerical encoding of itself, education_num. Hours-per-week was the feature that took occupation's place. I believe the primary reason for this phenomenon is due to the existense of hourly-salary system in US. An individual could have a high hourly salary, but he/she does not work long enough hours to accumulative enough earnings to add to his/her total earnings. Therefore, hours-per-week is the more prominent feature to occupation.
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.
# 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)))
Final Model trained on full data ------ Accuracy on testing data: 0.8653 F-score on testing data: 0.7403 Final Model trained on reduced data ------ Accuracy on testing data: 0.8486 F-score on testing data: 0.7066
Answer:
The final model's F-score and accuracy score on the reduced data are lower compared to the scores on the full-feature set. Even though the top 5 features can explain up to more than 70% of vairation in the model, removing the rest of the features will definitely diminish the performance. However, the performance after removing 8 extra features would only drop 2% in accuracy score and 4% in F-score, making it a worthy trade-off between training time. Reducing 8 features is equivalent with reducing the weight matrix by 36,177 x 8 elements. When training time is factor of consideration, using a leaner dataset without
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.