gradient boosting code example
Example 1: how to insert gradient in css
body{
/*Radial Gradient*/
background-image: radial-gradient(
/*Linear Gradient*/
background-image: linear-gradient(45.34deg,
}
Example 2: linear gradient css
/* A gradient tilted 100 degrees,
starting gray and finishing black */
.class {
background: linear-gradient(100deg, rgba(63, 63, 63, 0.8) 0%, rgba(255, 255, 255, 0) 25%);
}
/* A gradient going from the bottom to top
starting red and finishing orange */
.class {
background: linear-gradient(to top,
}
Example 3: Gradient-Boosted Trees (GBTs) learning algorithm for classification
from numpy import allclose
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import StringIndexer
df = spark.createDataFrame([
(1.0, Vectors.dense(1.0)),
(0.0, Vectors.sparse(1, [], []))], ["label", "features"])
stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
si_model = stringIndexer.fit(df)
td = si_model.transform(df)
gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed", seed=42)
model = gbt.fit(td)
model.featuresImportances
SparseVector(1, {0: 1.0})
allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1])
test0 = spark.createDataFrame([Vectors.dense(-1.0),)], ["features"])
model.transform(test0).head().prediction
test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
model.transform(test1).head().prediction
model.totalNumNodes
print(model.toDebugString)
gbtc_path = temp_path + "gbtc"
gbt.save(gbtc_path)
gbt2 = GBTClassifier.load(gbtc_path)
gbt2.getMaxDepth()
model_path = temp_path + "gbtc_model"
model.save(model_path)
model2 = GBTClassificationModel.load(model_path)
model.featureImportances == model2.featureImportances
model.treeWeights == model2.treeWeights
model.trees