– Open Machine Learning Course

Authors: Vitaly Radchenko (@vradchenko), Yury Kashnitsky (@yorko). Edited by Sergey Volkov (@sevaspb). This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

Assignment #3. Fall 2019

Part 2. Random Forest and Logistic Regression in credit scoring and movie reviews classification

Random Forest and logistic regression are two algorithms that I personally use most often in day-to-day DS tasks. In this part of the assignment, we'll explore pros and cons of these two algorithms in two different tasks.

Prior to working on the assignment, you'd better check out the corresponding course material:

  1. Classification, Decision Trees and k Nearest Neighbors, the same as an interactive web-based Kaggle Kernel
  2. Ensembles:
  3. You can also practice with demo assignments, which are simpler and already shared with solutions:
  4. There are also 7 video lectures on trees, forests, boosting and their applications:

Your task is to:

  1. write code and perform computations in the cells below
  2. choose answers in the webform (same one as for A3 part 1). Solutions will be shared only with those who've filled in this form.

Deadline for A3: 2019 October 27, 20:59 GMT (London time)

Okay, let's just cut the foreplay.


Predict whether the customer will repay their credit within 90 days. This is a binary classification problem; we will assign customers into good or bad categories based on our prediction.

Data description

Feature Variable Type Value Type Description
age Input Feature integer Customer age
DebtRatio Input Feature real Total monthly loan payments (loan, alimony, etc.) / Total monthly income percentage
NumberOfTime30-59DaysPastDueNotWorse Input Feature integer The number of cases when client has overdue 30-59 days (not worse) on other loans during the last 2 years
NumberOfTimes90DaysLate Input Feature integer Number of cases when customer had 90+dpd overdue on other credits
NumberOfTime60-89DaysPastDueNotWorse Input Feature integer Number of cased when customer has 60-89dpd (not worse) during the last 2 years
NumberOfDependents Input Feature integer The number of customer dependents
SeriousDlqin2yrs Target Variable binary:
0 or 1
Customer hasn't paid the loan debt within 90 days
In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Let us implement a function that will replace the NaN values by the median in each column of the table.

In [3]:
def impute_nan_with_median(table):
    for col in table.columns:
        table[col]= table[col].fillna(table[col].median())
    return table   

Reading the data:

In [4]:
data = pd.read_csv('../../data/credit_scoring_sample.csv', sep=";")
SeriousDlqin2yrs age NumberOfTime30-59DaysPastDueNotWorse DebtRatio NumberOfTimes90DaysLate NumberOfTime60-89DaysPastDueNotWorse MonthlyIncome NumberOfDependents
0 0 64 0 0.249908 0 0 8158.0 0.0
1 0 58 0 3870.000000 0 0 NaN 0.0
2 0 41 0 0.456127 0 0 6666.0 0.0
3 0 43 0 0.000190 0 0 10500.0 2.0
4 1 49 0 0.271820 0 0 400.0 0.0

View data types of the features:

In [5]:
SeriousDlqin2yrs                          int64
age                                       int64
NumberOfTime30-59DaysPastDueNotWorse      int64
DebtRatio                               float64
NumberOfTimes90DaysLate                   int64
NumberOfTime60-89DaysPastDueNotWorse      int64
MonthlyIncome                           float64
NumberOfDependents                      float64
dtype: object

Look at the distribution of the target variable:

In [6]:
ax = data['SeriousDlqin2yrs'].hist(orientation='horizontal', color='red')
ax.set_title("Target distribution")

print('Distribution of target:')
Distribution of target:
0    0.777511
1    0.222489
Name: SeriousDlqin2yrs, dtype: float64

Select all the features and drop the target:

In [7]:
independent_columns_names = data.columns.values
independent_columns_names = [x for x in data if x != 'SeriousDlqin2yrs']

We apply a function that replaces all NaN values with the median value of the corresponding feature.

In [8]:
table = impute_nan_with_median(data)

Split the target and features - now we get a training set.

In [9]:
X = table[independent_columns_names]
y = table['SeriousDlqin2yrs']


Part 1 had 7 questions, so here we start with #8.

Question 8. Make an interval estimate based on the bootstrap (2000 samples) of the average income (MonthlyIncome) of customers who had overdue loan payments, and of those who paid in time, make 80% confidence interval. Use target value (SeriousDlqin2yrs) to split data. Find the difference between the lower limit of the derived interval for those who paid in time and the upper limit for those who are overdue. So, you are asked to build 80% intervals for the income of "good" customers $ [good\_income\_lower, good\_income\_upper] $ and for "bad" - $ [bad\_income\_lower, bad\_income\_upper] $ and find the difference $ good\_income\_lower - bad\_income\_upper $.

Use the example from the article. Set np.random.seed(17). Round your answer to the closest integer.

Answer options:

  • 686
  • 734
  • 834
  • 996

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [10]:
# you'll be asked to fix this seed (`random_state`) everywhere in this notebook
SEED = 17
In [11]:
# You code here

Decision tree, hyperparameter tuning

One of the main performance metrics of a model is the area under the ROC curve. The ROC-AUC values lay between 0 and 1. The closer the value of ROC-AUC to 1, the better the classification is done.

Find the values of DecisionTreeClassifier hyperparameters using the GridSearchCV, which maximize the area under the ROC curve.

In [12]:
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV, StratifiedKFold

Use the DecisionTreeClassifier class to create a decision tree. Due to the imbalance of the classes in the target, we add the balancing parameter. We also use the parameter random_state = 17 for the reproducibility of the results.

In [13]:
dt = DecisionTreeClassifier(random_state=SEED, class_weight='balanced')

We will look through such values of hyperparameters:

In [14]:
max_depth_values = [5, 6, 7, 8, 9]
max_features_values = [4, 5, 6, 7]
tree_params = {'max_depth': max_depth_values,
               'max_features': max_features_values}

Fix cross-validation parameters: stratified, 5 partitions with shuffle, random_state. We will use this splitting throughout the notebook.

In [15]:
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)

Question 9. Run GridSearch with the ROC AUC metric using the hyperparameters from the tree_params dictionary. What is the maximum ROC AUC value (round up to 2 decimals)? We call cross-validation stable if the standard deviation of the metric on the cross-validation is less than 0.01. Was cross-validation stable under optimal combinations of hyperparameters (i.e., providing a maximum of the mean ROC AUC value for cross-validation)?

Answer options:

  • 0.82, no
  • 0.84, no
  • 0.82, yes
  • 0.84, yes

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [16]:
# You code here

Simple RandomForest implementation

Question 10. Implement your own random forest using DecisionTreeClassifier with the best parameters from the previous task. There will be 10 trees, the predicted probabilities of which you need to average.

Brief specification:

  • Use the base code below
  • In the fit method in the loop (i from 0 to n_estimators-1):
    • fix the seed equal to (random_state + i). The idea is that at each iteration there's a new value of random seed to add more "randomness", but at the same time results are reproducible
    • After fixing the seed, select max_features features without replacement, save the list of selected feature ids in self.feat_ids_by_tree
    • Also make a bootstrap sample (i.e. sampling with replacement) of training instances. For that, resort to np.random.choice and its argument replace
    • Train a decision tree with specified (in a constructor) arguments max_depth, max_features and random_state (do not specify class_weight) on a corresponding subset of training data.
  • The fit method returns the current instance of the class RandomForestClassifierCustom, that is self
  • In the predict_proba method, we need to loop through all the trees. For each prediction, obviously, we need to take only those features which we used for training the corresponding tree. The method returns predicted probabilities (predict_proba), averaged for all trees

Perform cross-validation with StratifiedKFold. What is the average cross-validation ROC AUC of the custom Random Forest implementation? Select the closest value.

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

Answer options:

  • 0.823
  • 0.833
  • 0.843
  • 0.853
In [17]:
from sklearn.base import BaseEstimator
from sklearn.model_selection import cross_val_score

class RandomForestClassifierCustom(BaseEstimator):
    def __init__(self, n_estimators=10, max_depth=10, max_features=10, 
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features
        self.random_state = random_state
        self.trees = []
        self.feat_ids_by_tree = []
    def fit(self, X, y):
        # You code here

    def predict_proba(self, X):
        # You code here
In [18]:
# You code here

Question 11. Let us compare our own implementation of a random forest with sklearn version of it. To do this, use RandomForestClassifier (class_weight='balanced', n_estimators=10, random_state=17), specify all the same values for max_depth and max_features as before. What average value of ROC AUC on cross-validation we finally got? Select the closest value.

Answer options:

  • 0.814
  • 0.827
  • 0.843
  • 0.856

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [19]:
from sklearn.ensemble import RandomForestClassifier
In [20]:
# You code here

sklearn RandomForest, hyperparameter tuning

Question 12. In the third task, we found the optimal hyperparameters for one tree. However it could be that these parameters are not optimal for an ensemble. Let's check this assumption with GridSearchCV (RandomForestClassifier (class_weight='balanced', n_estimators=10, random_state=17) ). Now we extend the value of max_depth up to 15, because the trees need to be deeper in the forest (you should be aware of it from the article). What are the best values of hyperparameters now?

Answer options:

  • max_depth=8, max_features=4
  • max_depth=9, max_features=5
  • max_depth=10, max_features=6
  • max_depth=11, max_features=7

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [21]:
max_depth_values = range(5, 15)
max_features_values = [4, 5, 6, 7]
forest_params = {
    'max_depth': max_depth_values,
    'max_features': max_features_values
In [22]:
# You code here

Logistic regression, hyperparameter tuning

Question 13. Now let's compare our results with logistic regression (we indicate class_weight = 'balanced', solver='liblinear' and random_state=17). Do a full search by the parameter C from a wide range of values np.logspace (-8, 8, 17). Now we will build a pipeline - first apply scaling, then train the model.

Learn about the pipelines and make cross-validation. What is the best average ROC AUC? Select the closest value.

Answer options:

  • 0.788
  • 0.798
  • 0.808
  • 0.818

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [23]:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

scaler = StandardScaler()
logit = LogisticRegression(random_state=SEED, solver='liblinear', class_weight='balanced')

logit_pipe = Pipeline([('scaler', scaler), ('logit', logit)])
logit_pipe_params = {'logit__C': np.logspace(-8, 8, 17)}
In [24]:
# You code here

Logistic regression and RandomForest on sparse features

In case of a small number of features, random forest was proved to be better than logistic regression. However, one of the main disadvantages of trees is how they work with sparse data, for example, with texts. Let's compare logistic regression and random forest in a new task. Download the dataset with movie reviews from here.

In [25]:
# Download data
df = pd.read_csv("../../data/", nrows=50000)

# Split data to train and test
X_text = df["text"]
y_text = df["label"]

# Classes counts
1    32492
0    17508
Name: label, dtype: int64
In [26]:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline

# Split on 3 folds
skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED)

# In Pipeline we will modify the text and train Random forest
classifier = Pipeline([
    ('vectorizer', CountVectorizer(max_features=100000, ngram_range=(1, 3))),
    ('clf', RandomForestClassifier(n_estimators=10, random_state=SEED, n_jobs=-1))])

min_samples_leaf = [1, 2, 3]
max_features = [0.3, 0.5, 0.7]
max_depth = [None]

Question 14. Let's use Random forest algorithm for this task. Similarly, look over all the values and get the maximum ROC AUC, select the closest value. Keep in mind that in this case training may take a lot of time (up to an hour).

Answer options:

  • 0.71
  • 0.75
  • 0.81
  • 0.85

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [27]:
# You code here

Question 15. Will Logistic Regression save our time? For Logistic Regression: iterate parameter C with values from the list [0.1, 1, 10, 100] and find the best ROC AUC in cross-validation. Select the closest answer.

Answer options:

  • 0.71
  • 0.75
  • 0.81
  • 0.85

For discussions, please stick to ODS Slack, channel #mlcourse_ai_news, pinned thread #a3_part2_fall2019

In [ ]:
# In Pipeline we will modify the text and train logistic regression
classifier = Pipeline([
    ('vectorizer', CountVectorizer(max_features=100000, ngram_range=(1, 3))),
    ('clf', LogisticRegression(solver='liblinear', random_state=SEED))])

parameters = {'clf__C': (0.1, 1, 10, 100)}
In [29]:
# You code here