1、Python代码 #导入需要的库fromlightgbmimportLGBMClassifierfromsklearnimportdatasetsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score,precision_score,recall_score,f1_score,confusion_matriximportmatplotlib.pyplotaspltimportseabornassnsimportnumpyasnp# 导入sklearn的iris数据集,...
步骤1 :运行以下代码得到训练集和验证集 import pandas as pd, numpy as np, time from sklearn.model_selection import train_test_split # 读取数据 data = pd.read_csv("https://cdn.coggle.club/kaggle-f…
使用train_test_split将数据集分割为训练集和验证集。 步骤3:模型训练 使用LightGBM进行训练。需要创建一个LightGBM数据集并定义参数。 import lightgbm as lgb # 创建LightGBM数据集 train_data = lgb.Dataset(X_train, label=y_train) valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data...
# 加载数据 data = load_breast_cancer() X = data.data y = data.target # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 将数据转换为LightGBM的Dataset格式 train_data = lgb.Dataset(X_train, label=y_train) # 定义模型参数...
dftrain,dftest = train_test_split(df) categorical_features = ['mean_radius','mean_texture'] lgb_train = lgb.Dataset(dftrain.drop(['label'],axis = 1),label=dftrain['label'], categorical_feature = categorical_features) lgb_valid = lgb.Dataset(dftest.drop(['label'],axis = 1),label...
from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 导入iris数据集 iris = load_iris() data, target = iris.data, iris.target # 数据集划分 X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=43) ...
['target']X_train,X_valid,y_train,y_valid=train_test_split(X,y,test_size=0.2,random_state=1234)train_data=lgb.Dataset(X_train,label=y_train)valid_data=lgb.Dataset(X_valid,label=y_valid,reference=train_data)params={'metric':FIXED_PARAMS['metric'],'objective':FIXED_PARAMS['objective'...
train_data_all,test_data,train_y_all,test_y = \ train_test_split(X, y,test_size=0.2,random_state=1,shuffle=True,stratify=iris.target) # 再从训练集中分割出训练集与验证集 train_data,val_data,train_y,val_y = \ train_test_split(train_data_all, train_y_all,test_size=0.2,random_...
X_train,X_test,y_train,y_test=train_test_split(data,target,test_size=0.2)# 加载你的数据 #print('Load data...')# df_train=pd.read_csv('../regression/regression.train',header=None,sep='\t')# df_test=pd.read_csv('../regression/regression.test',header=None,sep='\t')# ...
model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder import lightgbm as lgb import numpy as np # 以分隔符,读取文件,得到的是一个二维列表 iris = np.loadtxt('iris.data', dtype=str, delimiter=',', unpack=False, encoding='utf-8') # 前4列是特征 data = ...