random

Section I: Brief Introduction on Random Forest Random forests have gained huge popularity om applications of machine learning during the last decade due to their good classification performance,scalability, and ease of use . Intuitively, a random forest can be considered as an ensemble of decoson trees. The idea behind a random forest is to average multiple trees that individually suffer from high variance, to build a more robust model that has a better generalization performance and is less susceptible to overfitting. The major steps are summarized here: Step 1: Draw a random boostrap sample

Section I: Brief Introduction on Random Forest Random forests have gained huge popularity om applications of machine learning during the last decade due to their good classification performance,scalability, and ease of use . Intuitively, a random forest can be considered as an ensemble of decoson trees. The idea behind a random forest is to average multiple trees that individually suffer from high variance, to build a more robust model that has a better generalization performance and is less susceptible to overfitting. The major steps are summarized here: Step 1: Draw a random boostrap sample

Section I: Brief Introduction on Random Forest Random forests have gained huge popularity om applications of machine learning during the last decade due to their good classification performance,scalability, and ease of use . Intuitively, a random forest can be considered as an ensemble of decoson trees. The idea behind a random forest is to average multiple trees that individually suffer from high variance, to build a more robust model that has a better generalization performance and is less susceptible to overfitting. The major steps are summarized here: Step 1: Draw a random boostrap sample

二项分布 n次独立随机试验,成功概率p,定义变量X,表示成功的次数k( κ ∈ [ 0 , n ] ),则分布P(X=k): p ( k ) = ( n k ) p k p n − k 特别的例子就是,抛硬币.做100次抛硬币试h验(抛10次硬币)你会发现这一百次的试验,所记录的k次成功,k有高,有低,(0,10)之间.直觉告诉我,这个k的分布接近正态分布. 当日常说,人的智商接近正态分布.随机变量是由什么的随机事件映射成? - 这里的数学thoughts 1. Events Algebra.Set Theory. complex events → simplified events.something like ,多项式化简. 2. Probability measure not just about ∑ , 0 ≤ P ( A ) ≤ 1 ,它们只是表示的符号.内容,比如0,1之间,事件的和的概率是事件概率的和；才是概率测度的属性.这个公理和欧几里得公理一样,也是历史的归纳得出.和我们计算组合数一样也是归纳出来的. 3. Reflection 随机变量random variable X ( u ) : u → R 其中 U ( u ) ⊂ Ω . - distribution function: F ( x ) = P ( X < x ) , − ∞ < x < + ∞

参考 https://stackoverflow.com/questions/48471632/sklearn-pca-random-state-parameter-function Sklearn库给PCA方法添加了一个新的参数random_state，如下链接 http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html random_state : int, RandomState instance or None, optional (default None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when svd_solver == ‘arpack’ or ‘randomized’. 这个参数在如下中描述 Finding Structure With

常用的7个函数： 1. random.random()： 返回一个[0,1)之间的随机浮点值(双精度) 2. random. uniform(a,b)： 返回[a,b]之间的一个随机浮点；a,b两个变量大小随意 3. random.randrange([start],end,[step])： 返回[start,end)的范围之间的一个随机整数，考虑步长 4. random.randint(a,b)： 返回[a,b]之间的一个随机整数 5. random.choice(seq)： 随机获取seq中的某个元素返回 6. random.sample(seq,num)： 从seq对象中随机获取num个元素，以列表的形式返回 7. random.shuffle(lt)： 将list对象lt中的元素打乱 【注意】只能接受list对象 1 import random 2 3 print(random.random()) 4 5 print(random.uniform(10,20)) 6 7 print(random.randrange(0,10,2)) 8 9 print(random.randint(1,3)) 10 11 print(random.choice('abcdefg')) 12 13 print(random.sample(('a','b','c','d','e','f'),3