小编Ker*_*mit的帖子

我不知道如何确定DT::renderDataTable我的盒子适合我的尺寸。

这是我的闪亮渲染的图片

有人知道如何确保桌子适合盒子吗？或者我可以在表格下方添加一个滑块来滚动屏幕上没有的其他变量吗？

这是我的代码：

服务器R

output$table = DT::renderDataTable({
    DT::datatable(
      round(df,2), 
      rownames = TRUE,
      extensions = 'Buttons',
      options = list(
        autoWidth = FALSE, 
        columnDefs = list(list(width = "125px", targets = "_all")),
        dom = 'tpB',
        lengthMenu = list(c(5, 15, -1), c('5', '15', 'All')),
        pageLength = 15,
        buttons = list(
          list(
            extend = "collection",
            text = 'Show More',
            action = DT::JS("function ( e, dt, node, config ) {
                              dt.page.len(50);
                              dt.ajax.reload();}")
          ),list(
            extend = "collection",
            text = 'Show Less',
            action = DT::JS("function …

我是 scikit-learn 和随机森林回归的新手，想知道除了组合预测之外，是否有一种简单的方法可以从随机森林中的每棵树获得预测。

基本上我想在 R 中使用该predict.all = True选项可以做什么。

# Import the model we are using
from sklearn.ensemble import RandomForestRegressor
# Instantiate model with 1000 decision trees
rf = RandomForestRegressor(n_estimators = 1000, random_state = 1337)
# Train the model on training data
rf.fit(train_features, train_labels)
# Use the forest's predict method on the test data
predictions = rf.predict(test_features)
print(len(predictions)) #6565 which is the number of observations my test set has.

我想对每棵树进行每一次预测，而不仅仅是每个预测的平均值。

可以在python中做到吗？

我正在尝试将 XGBoost 的决策树保存为.png文件。当我使用随机森林执行此操作时效果很好，但它不适用于我的 XGBoost

我有以下代码：

import xgboost as xgb
from sklearn.tree import export_graphviz

import warnings
warnings.filterwarnings('ignore')

from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from math import sqrt

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
from sklearn import metrics

from sklearn.preprocessing import LabelEncoder


# Using Skicit-learn to split data into training and testing sets
from sklearn.model_selection import train_test_split

df = pd.read_csv("data_clean.csv")
del df["Unnamed: 0"]

df = df[["gross_square_feet","block","land_square_feet","lot","age_of_building","borough","residential_units","commercial_units","total_units","sale_price"]]

df['borough'] = …