regression

How to get the sum of least squares/error from polyfit in one dimension Python

由 柔情痞子 提交于 2021-02-11 07:16:42
问题 I want to do a linear regression for a scatter plot using polyfit, and I also want the residual to see how good the linear regression is. But I am unsure how I get this as it isn't possible to get the residual as an output value from polyfit since this is one dimensional. My code: p = np.polyfit(lengths, breadths, 1) m = p[0] b = p[1] yfit = np.polyval(p,lengths) newlengths = [] for y in lengths: newlengths.append(y*m+b) ax.plot(lengths, newlengths, '-', color="#2c3e50") I saw a stackoverflow

How to get the sum of least squares/error from polyfit in one dimension Python

由 折月煮酒 提交于 2021-02-11 07:16:25
问题 I want to do a linear regression for a scatter plot using polyfit, and I also want the residual to see how good the linear regression is. But I am unsure how I get this as it isn't possible to get the residual as an output value from polyfit since this is one dimensional. My code: p = np.polyfit(lengths, breadths, 1) m = p[0] b = p[1] yfit = np.polyval(p,lengths) newlengths = [] for y in lengths: newlengths.append(y*m+b) ax.plot(lengths, newlengths, '-', color="#2c3e50") I saw a stackoverflow

How to get the sum of least squares/error from polyfit in one dimension Python

由 和自甴很熟 提交于 2021-02-11 07:16:10
问题 I want to do a linear regression for a scatter plot using polyfit, and I also want the residual to see how good the linear regression is. But I am unsure how I get this as it isn't possible to get the residual as an output value from polyfit since this is one dimensional. My code: p = np.polyfit(lengths, breadths, 1) m = p[0] b = p[1] yfit = np.polyval(p,lengths) newlengths = [] for y in lengths: newlengths.append(y*m+b) ax.plot(lengths, newlengths, '-', color="#2c3e50") I saw a stackoverflow

R-squared within for a regression with multiple fixed effects [closed]

由 筅森魡賤 提交于 2021-02-11 06:30:32
问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 10 months ago . Improve this question I would like to get the R-squared within for a fixed effect regression with multiple fixed effects (let's say Country, Year, Trimester). The least squared dummy variable (LSDV) model ( lm in R/ reg in Stata) would only provide the overall R-squared. The same is

【笔记】机器学习

由 萝らか妹 提交于 2021-02-11 05:29:34
学习笔记： 章节 简介 1 - Introduction & next step 机器学习介绍 & 机器学习下一步 2 - Regression + Demo 回归 & 示例代码 3 - Bias & Variance 偏差和方差 4 - Gradient Descent 梯度下降方法 5 - Classification 分类 6 - Logistic Regression 对数几率回归（逻辑回归） 7 - Deep Learning 深度学习介绍 8 - Backpropagation 反向传播 9 - Keras Demo Keras代码示例 10 - Tips for Training DNN 深度学习技巧 11 - Keras Demo2 & Fizz Buzz Keras代码优化 12 - CNN 卷积神经网络 13 - Why Deep 为什么要用深度学习 Explainable ML 可解释性机器学习 Transformer Transformer及注意力机制 ELMO、BERT、GPT ELMO、BERT、GPT-2、ERNIE 课后作业： Week 1: 矩阵运算，图片操作 Week 2: CEO利润预测 论文阅读： An overview of gradient descent optimization algorithms 参考资料： 李宏毅 2017

Normalizing columns in R according to a formula

由 折月煮酒 提交于 2021-02-11 03:21:18
问题 Let's say I have a data frame of 1000 rows and 3 columns (column t0, t4 and t8). Each column represents a time point (0 hours, 4 hours and 8 hours). The data is gene expression: numeric (float): row.name t0 t4 t8 ENSG00000000419.8 1780.00 1837.00 1011.00 ENSG00000000457.9 859.00 348.39 179.00 ENSG00000000460.12 1333.00 899.00 508.00 I need to normalize the data according to a known result. I know that the average half-life of all rows (genes) should be 10 hours. So I need to find the

Regression line using Relplot in seaborn

由 老子叫甜甜 提交于 2021-02-10 20:00:35
问题 Below is a working example where I need to draw regression line. I have searched online but I see another function like regplot, implot to draw regression lines but here I am using replot. How I can draw regression line using relplot? from matplotlib import pyplot as plt import pandas as pd import seaborn as sns d = {'x-axis':[100,915,298,299], 'y-axis': [1515,1450,1313,1315], 'text':['point1','point2','point3','point4']} df = pd.DataFrame(d) p1 = sns.relplot(x='x-axis', y='y-axis',data=df )

plotting a fitted segmented linear model shows more break points than what is estimated

由 不问归期 提交于 2021-02-10 18:53:51
问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

plotting a fitted segmented linear model shows more break points than what is estimated

由 有些话、适合烂在心里 提交于 2021-02-10 18:51:20
问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

Tensorflow keras timeseries prediction with X and y having different shapes

由 不羁的心 提交于 2021-02-10 15:44:10
问题 I am trying to do time series prediction with tensorflow and keras with X and y having different dimensions: X.shape = (5000, 12) y.shape = (5000, 3, 12) When I do the following n_input = 7 generator = TimeseriesGenerator(X, y, length=n_input, batch_size=1) for i in range(5): x_, y_ = generator[i] print(x_.shape) print(y_.shape) I get as desired the output (1, 7, 12) (1, 3, 12) (1, 7, 12) (1, 3, 12) ... This is because my data is meteorological, I have 5000 days, for training in the array X I
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