April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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204
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第七章：整體學習與隨機森林
你也可以讓梯度增強使用其他的代價函數
。
這是用
loss
超參數來控制的
（
詳情見
Scikit-Learn
的文件
）。
值得一提的是
，
熱門的
Python
程式庫
XGBoost（https://github.com/dmlc/xgboost）
提供了優
化的梯度增強實作
，XGBoost
是
Extreme Gradient Boosting
的縮寫
。
這個程式庫最初是由
Distributed（Deep）Machine Learning Community（DMLC）
的
Tianqi Chen
開發的
，
期望
有極快的速度
、
極好的擴展性和可移植性
。
事實上
，XGBoost
往往是各種
ML
競賽獲勝作
品的重要元素
。XGBoost
的
API
很像
Scikit-Learn
的
：
import xgboost
xgb_reg = xgboost.XGBRegressor()
xgb_reg.fit(X_train, y_train)
y_pred = xgb_reg.predict(X_val)
XGBoost
也提供了一些很棒的功能
，
例如自動處理提前停止
：
xgb_reg.fit(X_train, y_train,
eval_set=[(X_val, y_val)], early_stopping_rounds=2)
y_pred = xgb_reg.predict(X_val)
你一定要好好研究它
！
stacking
本章探討的最後一種整體方法稱為
stacking（stacked generalization
的簡稱 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.