1.
Using machine learning
to determine drivers
of bounce and conversion
2016 Velocity Santa Clara

2.
Pat Meenan
@patmeenan
Tammy Everts
@tameverts

3.
What we did

4.
Get the code
https://github.com/WPO-Foundation/beacon-ml

5.
Deep Learning
Weights

6.
Random Forest
Lots of random decision trees

7.
Vectorizing the data
• Everything needs to be numeric
• Strings converted to several inputs as
yes/no (1/0)
• i.e. Device Manufacturer
– “Apple” would be a discrete input
• Watch out for input explosion (UA String)

8.
Balancing the data
• 3% Conversion Rate
• 97% Accurate by always guessing no
• Subsample the data for 50/50 mix

9.
Validation Data
• Train on 80% of the data
• Validate on 20% to prevent overfitting

10.
Smoothing the data
• ML works best on normally distributed data
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_val = scaler.transform(x_val)

11.
Input/Output Relationships
• SSL highly correlated with Conversions
• Long sessions highly correlated with not
bouncing
• Remove correlated features from training

12.
Training Deep Learning
model = Sequential()
model.add(...)
model.compile(optimizer='adagrad',
loss='binary_crossentropy',
metrics=["accuracy"])
model.fit(x_train,
y_train,
nb_epoch=EPOCH_COUNT,
batch_size=32,
validation_data=(x_val, y_val),
verbose=2,
shuffle=True)

13.
Training Random Forest
clf = RandomForestClassifier(n_estimators=FOREST_SIZE,
criterion='gini',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
max_leaf_nodes=None,
bootstrap=True,
oob_score=False,
n_jobs=12,
random_state=None,
verbose=2,
warm_start=False,
class_weight=None)
clf.fit(x_train, y_train)

14.
Feature Importances
clf.feature_importances_

15.
What we learned

16.
Takeaways

17.
Thanks!