linear regression

Tutorials

Linear Regression with Python | Connor Johnson

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numpy.polyfit

code:python

np.polyfit(x,y,deg=1)

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scipy.stats.linregress

scipy.optimize.curve_fit

code:python

from scipy import optimize

# Define a model function

def model_func(x, a0, a1):

return a0 + (a1*x)

param_opt, param_cov = optimize.curve_fit(model_func, x_data, y_data)

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examples

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Statsmodels Examples

Linear Regression

Fitting models using R-style formulas

Examples

code:python

import pandas as pd

from statsmodels.formula.api import ols

df = pd.DataFrame(dict(x_column=x_data, y_column=y_data))

model_fit = ols(formula="y_column ~ x_column", data=df).fit()

# Use .predict(df) to get y_model values, then over-plot y_data with y_model

y_model = model_fit.predict(df)

# Extract the a0, a1 values from model_fit.params

Regression beteeen the Kushimoto-Uragami sea-level difference and Kuroshio-Path latitude (on Google Colab)

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sklearn.linear_model.LinearRegression

Examples

code:python

from sklearn.linear_model import LinearRegression

# Initialize a general model

model = LinearRegression(fit_intercept=True)

# Load and shape the data

x_data = x_raw.reshape(len(y_raw),1)

y_data = y_raw.reshape(len(y_raw),1)

# Fit the model to the data

model_fit = model.fit(x_data, y_data)

# Extract the linear model parameters

# Extract the linear model parameters

This week we explore some machine learning basics on a new MetPyMonday!

sklearn.linear_model.Ridge

Tutorials

一般化線形モデルのラッソとリッジでの推定量算出・予測 - Qiita

Paid tutorials

Machine Learning for Time Series Data in Python | DataCamp

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See also