sklearn-pandas

First there are questions on this forum very similar to this one but trust me none matches so no duplicating please. I have encountered two methods of linear regression using scikit's sklearn and I am failing to understand the difference between the two, especially where in first code there's a method train_test_split() called while in the other one directly fit method is called. I am studying with multiple resources and this single issue is very confusing to me. First which uses SVR X = np.array(df.drop(['label'], 1)) X = preprocessing.scale(X) y = np.array(df['label']) X_train, X_test, y

i am having the following information(dataframe) in python product baskets scaling_factor 12345 475 95.5 12345 108 57.7 12345 2 1.4 12345 38 21.9 12345 320 88.8 and I want to run the following non-linear regression and estimate the parameters. a ,b and c Equation that i want to fit: scaling_factor = a - (b*np.exp(c*baskets)) In sas we usually run the following model:(uses gauss newton method ) proc nlin data=scaling_factors; parms a=100 b=100 c=-0.09; model scaling_factor = a - (b * (exp(c*baskets))); output out=scaling_equation_parms parms=a b c; is there a similar way to estimate the

I just started learning machine learning, when practicing one of the task, I am getting value error, but I followed the same steps as the instructor does. I am getting value error, please help. dff Country Name 0 AUS Sri 1 USA Vignesh 2 IND Pechi 3 USA Raj First I performed labelencoding, X=dff.values label_encoder=LabelEncoder() X[:,0]=label_encoder.fit_transform(X[:,0]) out: X array([[0, 'Sri'], [2, 'Vignesh'], [1, 'Pechi'], [2, 'Raj']], dtype=object) then performed One hot encoding for the same X onehotencoder=OneHotEncoder( categorical_features=[0]) X=onehotencoder.fit_transform(X).toarray

The goal is to open in Java a model created/trained in python with tensorflow.contrib.learn.learn.DNNClassifier . At the moment the main issue is to know the name of the "tensor" to give in java on the session runner method. I have this test code in python : from __future__ import division, print_function, absolute_import import tensorflow as tf import pandas as pd import tensorflow.contrib.learn as learn import numpy as np from sklearn import metrics from sklearn.cross_validation import train_test_split from tensorflow.contrib import layers from tensorflow.contrib.learn.python.learn.utils

I'm trying to do a clustering with K-means method but I would like to measure the performance of my clustering. I'm not an expert but I am eager to learn more about clustering. Here is my code : import pandas as pd from sklearn import datasets #loading the dataset iris = datasets.load_iris() df = pd.DataFrame(iris.data) #K-Means from sklearn import cluster k_means = cluster.KMeans(n_clusters=3) k_means.fit(df) #K-means training y_pred = k_means.predict(df) #We store the K-means results in a dataframe pred = pd.DataFrame(y_pred) pred.columns = ['Species'] #we merge this dataframe with df

I am trying this training modeling using train_test_split and a decision tree regressor: import sklearn from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import cross_val_score # TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature new_data = samples.drop('Fresh', 1) # TODO: Split the data into training and testing sets using the given feature as the target X_train, X_test, y_train, y_test = train_test_split(new_data, samples['Fresh'], test_size=0.25, random_state=0) # TODO: Create

I tried to do the following importations for a machine learning project: from sklearn import preprocessing, cross_validation, svm from sklearn.linear_model import LinearRegression I got this error message: Traceback (most recent call last): File "C:/Users/Abdelhalim/PycharmProjects/ML/stock pricing.py", line 4, in <module> from sklearn import preprocessing, cross_validation, svm File "C:\Python27\lib\site-packages\sklearn\__init__.py", line 57, in <module> from .base import clone File "C:\Python27\lib\site-packages\sklearn\base.py", line 12, in <module> from .utils.fixes import signature File