Sign uporlog into customize your list. Start here for a quick overview of the site Detailed answers to any questions you might have Discuss the workings and policies of this site Learn more about Stack Overflow the company Learn more about hiring developers or posting ads with us Cross Validated is a question and answer […]

Read More → correlation

Linear Regression without GridSearch from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score, cross_val_predict X = [[Some data frame of predictors]] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3) 0.3 is standard test size, pick what you need to print X_test.shape, y_test.shape Its good practice to check Next we do […]

Read More → Untitled

Linear regression Sklearn, Keras, Theano & Lasagne 679bad9b361d9e45055d8a036b027deb0c47019e + LINEAR REGRESSION WITH SKLEARN LIBRARY +from sklearn.datasets import load_boston +import statsmodels.formula.api as sm +from keras.regularizers import l2, activity_l2 +x_train,y_train=dataset.data,dataset.target +for i in range(0,x_train.shape[1]): + rrcoef(x_train.T[i],y_train)[0][1]) +d=np.where(abs(np.array(c)).5) + z.append(np.where(x.T[i]2*np.std(x.T[i]))) + return (x-min(x))/(max(x)-min(x)) +y2=np.array(norm(y_train[z2[0][0]])) +1-np.mean(abs(linear.predict(x2)-y2)) +plt.scatter(residuos,linear.predict(x2)) +T=np.array(list(range(0,len(y2)))) +plt.plot(linear.predict(x2),.,color=r) +linn=linear_model.LinearRegression() +pred=linn.intercept_+linn.coef_[0]*x2.T[0]+linn.coef_[1]*x2.T[1]+linn.coef_[2]*x2.T[2] + LINEAR REGRESSION USING KERAS +from keras.models import […]

Read More → Linear regression Sklearn Keras Theano Lasagne