porting an example from tensorflow about yggdrasil-decision-forests HOT 4 CLOSED

Hi, you can train directly on multi-dimensional numpy data as explained in the documentation: https://ydf.readthedocs.io/en/latest/tutorial/multidimensional_feature

The super short version of it is (with random data)

import ydf
num_examples = 10000
num_rows = 20
train_data = np.random.uniform(size=(num_examples, num_rows))
train_label = np.random.randint(0, 2, size=(num_examples))

train_ds = {"features": train_data, "label": train_label}

model = ydf.GradientBoostedTreesLearner(label="label").train(train_ds)

test_data = {"features": np.random.uniform(size=(1, num_rows))}

model.predict(test_data)

from yggdrasil-decision-forests.

Hi,
Thanks for the tip.
I have tried as you suggested but prediction values are like random values between 0.0 and 1.0, not at all useful.

from yggdrasil-decision-forests.

Ok, Here is the test. Extract files(train.npy, test.npy) from the attached zip file

import numpy as np
import ydf

train_data = np.load('train.npy')
train_label = np.random.randint(0, 2, size=(train_data.shape[0]))

print(train_data.shape)

train_ds = {"features": train_data, "label": train_label}
model = ydf.GradientBoostedTreesLearner(label="label").train(train_ds)
test_data = {"features": np.load('test.npy')}

predictions = model.predict(test_data)
print(predictions)

For the same data, TensorFlow's predictions are 99% correct but ydf's predictions look random to me. Am I missing something
here?
ydf.zip

from yggdrasil-decision-forests.

This notebook shows how to train a model on this dataset and make predictions with a Random Forest and a Gradient Boosted Trees model. The notebook also runs a cross-validation to evaluate the quality of predictions on this small dataset.

The model self evaluation (model.describe() ; out-of-bag accuracy of 53%) and cross-validation (learner.cross_validation(train_ds) ; accuracy=50%, AUC=51%) shows that the input features are virtually not correlated with the labels.

You mention that with "TensorFlow's predictions are 99% correct". Are you sure you are using the same dataset? If so, are you sure you are not evaluating on the training dataset?

from yggdrasil-decision-forests.