Rule-Based Prediction | BANA409 — Machine Learning for Business

Based on the EDA, we could develop simple rule-based predictions, which can serve as the baseline for the comparison with predictions based on machine learning models.

Let’s use the Titanic dataset as an example:

df_train = pd.read_csv('train.csv')
df_train.Survived.value_counts(normalize=True)

0    0.616162
1    0.383838

Given the overall survival rate, we can have the following rule-based baseline:

Baseline 1: predict all passengers are dead (survived = 0) because survival rate is only 38.4%

df_train.groupby('Sex').Survived.value_counts().to_frame()

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Based on the survival rates by gender, we can have another rule-based baseline:

Baseline 2: predict the all male passengers are dead and all female passengers survived, given female survival rate is 74.2% and male survival rate: 18.9%

Next, we will make predictions based on the two baselines and submit to Kaggle for scoring - we will learn how those scores are calculated in the following lectures on Model Evaluation.

Normally, Kaggle competition provides at least three files:

the training data (the target is included)
the testing data (you need to predict the target)
sample submission file (show you the format of your submission file)

Go to Titanic site (https://www.kaggle.com/c/titanic) and download the three files.

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From the sample submission file, we know only two columns are needed with names: PassengerId and Survived where PassengerId must match the PassengerId in test.csv file.

Create the submission file for Baseline 1:

# predict all passengers are dead (survived = 0) because survival rate is only 38.4%
df_test['Survived'] = 0  # create a new column
df_submit_baseline_1 = df_test[['PassengerId', 'Survived']]  # only two columns are needed

# save the dataframe to a csv file for submission
# index=False we don't need index to be in the csv file
df_submit_baseline_1.to_csv('submit_baseline_1.csv', index=False)

Submit the generated CSV file to Kaggle for scoring:

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Baseline 1 score is 0.622 (as we will learn later, this is accuracy - the higher the better)

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Now, let’s work on baseline 2: I show two ways to generate the target column (both are useful to learn):

use .loc[] and .astype()

# baseline 2: predict survived = 0 (dead) for male and survived = 1 (survived) for female 
# .loc create a new column and enter values based on the values of Sex
df_test.loc[df_test.Sex=='male', 'survived_baseline_2'] = 0
df_test.loc[df_test.Sex=='female', 'survived_baseline_2'] = 1

# .loc cast 0/1 to float 0.0/1.0, which needs to be converted back to integer
df_test.survived_baseline_2 = df_test.survived_baseline_2.astype(int)

use .apply()

# another way of generating the baseline 2 using .apply()
# first define a transformation function 
def survival_by_gender(gender):
    if gender == 'male':
        survival = 0
    else:
        survival = 1
    return survival

# apply the function to Sex column to create a new column
df_test['survived_baseline_2_apply'] = df_test.Sex.apply(survival_by_gender)

Then we generate the csv file:

df_submit_baseline_2 = df_test[['PassengerId', 'survived_baseline_2']]  # only two columns are needed

# save the dataframe to a csv file for submission
# header specifies the column names in the csv file
# index=False we don't need index to be in the csv file
df_submit_baseline_2.to_csv('submit_baseline_2.csv', header=['PassengerId', 'Survived'], index=False)

Submit to Kaggle for scoring. Baseline 2 gets 0.766, which beats Baseline 1.

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