大数据开发

活不多说,直接上代码/** * 多元线性回归算法 */public class Test { public static void main(String[] args) { int count = 100;//样本数量 double[] dimension = new double[]{11, 11, 1, -11, 4, 5};//最终权重 double[] dimensionCalc = new double[]{-2, 1, 0,

下面介绍sparkML中常用的算法与demo的使用方式,使用方式提供javaApipom依赖这里使用的版本spark为2.0.0 scala为2.11可以根据自己的需求调整 <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.11</artifactId>

1.随机森林import pandas as pdfrom sklearn.ensemble import RandomForestClassifierdata_values = pd.DataFrame(data=[[1, 2], [2, 3], [3, 4]], columns=["k1", "k2"]) # data表示数据, columns表示列字段名称model = RandomForestClassifier(max_depth=3, n_estimators=10) # max

pom依赖<dependency> <groupId>org.jfree</groupId> <artifactId>jfreechart</artifactId> <version>1.0.19</version></dependency><build&gt...

pom<dependencies> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>2.4.0&l...

pom<dependencies> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>2.4.0&l...

pom<dependencies> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>2.4.0&l...

pom<dependencies> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>2.4.0</...

pom依赖<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.12</artifactId> <version>2.4.0</version></dependency>...