【机器学习】随机森林是我最喜欢的模型
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TensorFlow 决策森林 (TF-DF) 现已开源,该库集成了众多 SOTA 算法,不需要输入特征,可以处理数值和分类特征,为开发者节省了大量时间。
对初学者来说,开发和解释决策森林模型更容易。不需要显式地列出或预处理输入特征(因为决策森林可以自然地处理数字和分类属性)、指定体系架构(例如,通过尝试不同的层组合,就像在神经网络中一样),或者担心模型发散。一旦你的模型经过训练,你就可以直接绘制它或者用易于解释的统计数据来分析它。
高级用户将受益于推理时间非常快的模型(在许多情况下,每个示例的推理时间为亚微秒)。而且,这个库为模型实验和研究提供了大量的可组合性。特别是,将神经网络和决策森林相结合是很容易的。
TF-DF 提供了一系列 SOTA 决策森林训练和服务算法,如随机森林、CART、(Lambda)MART、DART 等。
基于树的模型与各种 TensorFlow 工具、库和平台(如 TFX)更容易集成,TF-DF 库可以作为通向丰富 TensorFlow 生态系统的桥梁。
对于神经网络用户,你可以使用决策森林这种简单的方式开始 TensorFlow,并继续探索神经网络。
项目地址:https://github.com/tensorflow/decision-forests
TF-DF 网站地址:https://www.tensorflow.org/decision_forests
Google I/O 2021 地址:https://www.youtube.com/watch?v=5qgk9QJ4rdQ
# Install TensorFlow Decision Forests
!pip install tensorflow_decision_forests
# Load TensorFlow Decision Forests
import tensorflow_decision_forests as tfdf
# Load the training dataset using pandas
import pandas
train_df = pandas.read_csv("penguins_train.csv")
# Convert the pandas dataframe into a TensorFlow dataset
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species")
# Train the model
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)
# Load the testing dataset
test_df = pandas.read_csv("penguins_test.csv")
# Convert it to a TensorFlow dataset
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="species")
# Evaluate the model
model.compile(metrics=["accuracy"])
print(model.evaluate(test_ds))
# >> 0.979311
# Note: Cross-validation would be more suited on this small dataset.
# See also the "Out-of-bag evaluation" below.
# Export the model to a TensorFlow SavedModel
model.save("project/my_first_model")
tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0)
每个特性使用了多少次?
模型训练的速度有多快(树的数量和时间)?
节点在树结构中是如何分布的(比如大多数 branch 的长度)?
# Print all the available information about the model
model.summary()
7): Input Features (
>> bill_depth_mm
bill_length_mm
body_mass_g>>
...
Importance: Variable
1. "bill_length_mm" 653.000000 ################
...
evaluation: accuracy:0.964602 logloss:0.102378 Out-of-bag
trees: 300 Number of
nodes: 4170 Total number of
...
# Get feature importance as a array
model.make_inspector().variable_importances()["MEAN_DECREASE_IN_ACCURACY"]
"flipper_length_mm", 0.149), [(
>> ("bill_length_mm", 0.096),
>> ("bill_depth_mm", 0.025),
>> ("body_mass_g", 0.018),
>> ("island", 0.012)]
# List all the other available learning algorithms
tfdf.keras.get_all_models()
[tensorflow_decision_forests.keras.RandomForestModel,
>> tensorflow_decision_forests.keras.GradientBoostedTreesModel,
>> tensorflow_decision_forests.keras.CartModel]
# Display the hyper-parameters of the Gradient Boosted Trees model
? tfdf.keras.GradientBoostedTreesModel
"correct" for the errors of the previously trained trees (more precisely each tree predicts the gradient of the loss relative to the model output).. A GBT (Gradient Boosted [Decision] Tree) is a set of shallow decision trees trained sequentially. Each tree is trained to predict and then
...
Attributes:
num_trees: num_trees: Maximum number of decision trees. The effective number of trained trees can be smaller if early stopping is enabled. Default: 300.
max_depth: Maximum depth of the tree. `max_depth=1` means that all trees will be roots. Negative values are ignored. Default: 6.
...
# Create another model with specified hyper-parameters
model = tfdf.keras.GradientBoostedTreesModel(
num_trees=500,
growing_strategy="BEST_FIRST_GLOBAL",
max_depth=8,
split_axis="SPARSE_OBLIQUE"
,)
# Evaluate the model
model.compile(metrics=["accuracy"])
print(model.evaluate(test_ds))#
0.986851
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