So I have this small dataset and ı want to perform multiple linear regression on it.
first I drop the deliveries column for it's high correlation with miles. Although gasprice is supposed to be removed, I don't remove it so that I can perform multiple linear regression and not simple linear regression.
finally I removed the outliers and did the following:
Dataset
import math
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.stats import diagnostic as diag
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
from sklearn import linear_model
%matplotlib inline
X = dfafter
Y = dfafter[['hours']]
# Split X and y into X_
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1)
# create a Linear Regression model object
regression_model = LinearRegression()
# pass through the X_train & y_train data set
regression_model.fit(X_train, y_train)
y_predict = regression_model.predict(X_train)
#lets find out what are our coeffs of the multiple linear regression and olso find intercept
intercept = regression_model.intercept_[0]
coefficent = regression_model.coef_[0][0]
print("The intercept for our model is {}".format(intercept))
print('-'*100)
# loop through the dictionary and print the data
for coef in zip(X.columns, regression_model.coef_[0]):
print("The Coefficient for {} is {}".format(coef[0],coef[1]))
#Coeffs here don't match the ones that will appear later
#Rebuild the model using Statsmodel for easier analysis
X2 = sm.add_constant(X)
# create a OLS model
model = sm.OLS(Y, X2)
# fit the data
est = model.fit()
# calculate the mean squared error
odel_mse = mean_squared_error(y_train, y_predict)
# calculate the mean absolute error
model_mae = mean_absolute_error(y_train, y_predict)
# calulcate the root mean squared error
model_rmse = math.sqrt(model_mse)
# display the output
print("MSE {:.3}".format(model_mse))
print("MAE {:.3}".format(model_mae))
print("RMSE {:.3}".format(model_rmse))
print(est.summary())
#????????? something is wrong
X = df[['miles', 'gasprice']]
y = df['hours']
regr = linear_model.LinearRegression()
regr.fit(X, y)
print(regr.coef_)
So the code ends here. I found different coeffs every time I printed them out. what did I do wrong and is any of them correct?
I see you are trying 3 different things here, so let me summarize:
sklearn.linear_model.LinearRegression() with train_test_split(X, Y, test_size=0.2, random_state=1), so only using 80% of the data (but the split should be the same every time you run it since you fixed the random state)
statsmodels.api.OLS with the full dataset (you're passing X2 and Y, which are not cut up into train-test)
sklearn.linear_model.LinearRegression() with the full dataset, as in n2.
I tried to reproduce with the iris dataset, and I am getting identical results for cases #2 and #3 (which are trained on the same exact data), and only slightly different coefficients for case 1.
In order to evaluate if any of them are "correct", you will need to evaluate the model on unseen data and look at adjusted R^2 score, etc (hence you need the holdout (test) set). If you want to further improve the model you can try to understand better the interactions of the features in the linear model. Statsmodels has a neat "R-like" formula way to specify your model: https://www.statsmodels.org/dev/example_formulas.html
Related
I have been battling this problem with my MSE while predicting with regression. I have encountered the same problem with different regression models I have tried to build.
The problem is, my MSE is humongous. 83661743.99 to be exact. My R squared is 0.91 which does not seem problematic.
I manually implemented the cost function and gradient descent while doing the coursework in Andrew Ng's Stanford ML classes and I have a reasonable cost function; but when I try to implement it with SKLearn lib the MSE is something else. I don't know what I have done wrong and I need some help checking it out.
Here is the link to the dataset I used: https://www.kaggle.com/farhanmd29/50-startups
My code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
df = pd.read_csv('50_Startups.csv')
#checking the level of correlations between the predictors and response
sns.heatmap(df.corr(), annot=True)
#Splitting the predictors from the response
X = df.iloc[:,:-1].values
y = df.iloc[:,4].values
#Encoding the Categorical values
label_encoder_X = LabelEncoder()
X[:,3] = label_encoder_X.fit_transform(X[:,3])
#Feature Scaling
scaler = StandardScaler()
X = scaler.fit_transform(X)
#splitting train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
#Linear Regression
model = LinearRegression()
model.fit(X_train,y_train)
pred = model.predict(X_test)
#Cost Function
mse = mean_squared_error(y_test,pred)
mse
As you used standard normalization for scaling, the values of the dataset can be humongous. As desertnaut said, MSE is not scaled so it can be huge due to the big values of the dataset. You can try to normalize data using a MinMaxScaler to get the iput between [0-1]
I have gotten to understand the error of my ways. The MSE is 1/n (No of Samples) multiplied by the summation of the actual response subtracted by the predicted response SQUARED. Hence the error given will be SQUARED the expected error value. what I should have looked out for was the RMSE which will find the sqrt of the MSE. my predictions were off as well and that was because I scaled my values. Un-scaled X values gave me much better predictions. This I will have to look into more as I do not understand why.
I have a multi-class classification problem with 9 different classes. I am using the AdaBoostClassifier class from scikit-learn to train my model without using the one vs all technique, as the number of classes is very high and it might be inefficient.
I have tried using the tips from the documentation in scikit learn [1], but there the one vs all technique is used, which is substantially different. In my approach I only get one prediction per event, i.e. if I have n classes, the outcome of the prediction is a single value within the n classes. For the one vs all approach, on the other hand, the outcome of the prediction is an array of size n with a sort of likelihood value per class.
[1]
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py
The code is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt # Matplotlib plotting library for basic visualisation
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_curve, auc
from sklearn import preprocessing
# Read data
df = pd.read_pickle('data.pkl')
# Create the dependent variable class
# This will substitute each of the n classes from
# text to number
factor = pd.factorize(df['target_var'])
df.target_var= factor[0]
definitions = factor[1]
X = df.drop('target_var', axis=1)
y = df['target_var]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
bdt_clf = AdaBoostClassifier(
DecisionTreeClassifier(max_depth=2),
n_estimators=250,
learning_rate=0.3)
bdt_clf.fit(X_train, y_train)
y_pred = bdt_clf.predict(X_test)
#Reverse factorize (converting y_pred from 0s,1s, 2s, etc. to their original values
reversefactor = dict(zip(range(9),definitions))
y_test_rev = np.vectorize(reversefactor.get)(y_test)
y_pred_rev = np.vectorize(reversefactor.get)(y_pred)
I tried directly with the roc curve function, and also binarising the labels, but I always get the same error message.
def multiclass_roc_auc(y_test, y_pred):
lb = preprocessing.LabelBinarizer()
lb.fit(y_test)
y_test = lb.transform(y_test)
y_pred = lb.transform(y_pred)
return roc_curve(y_test, y_pred)
multiclass_roc_auc(y_test, y_pred_test)
The error message is:
ValueError: multilabel-indicator format is not supported
How could this be sorted out? Am I missing some important concept?
I have the following code so far:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn import preprocessing
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df_train = pd.read_csv('uc_data_train.csv')
del df_train['Unnamed: 0']
temp = df_train['size_womenswear']
del df_train['size_womenswear']
df_train['size_womenswear'] = temp
df_train['count'] = 1
print(df_train.head())
print(df_train.dtypes)
print(df_train[['size_womenswear', 'count']].groupby('size_womenswear').count()) # Determine number of unique catagories, and number of cases for each catagory
del df_train['count']
df_test = pd.read_csv('uc_data_test.csv')
del df_test['Unnamed: 0']
print(df_test.head())
print(df_test.dtypes)
df_train.drop(['customer_id','socioeconomic_status','brand','socioeconomic_desc','order_method',
'first_order_channel','days_since_first_order','total_number_of_orders', 'return_rate'], axis=1, inplace=True)
LE = preprocessing.LabelEncoder() # Create label encoder
df_train['size_womenswear'] = LE.fit_transform(np.ravel(df_train[['size_womenswear']]))
print(df_train.head())
print(df_train.dtypes)
x = df_train.iloc[:,np.arange(len(df_train.columns)-1)].values # Assign independent values
y = df_train.iloc[:,-1].values # and dependent values
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.25, random_state = 0) # Testing on 75% of the data
model = GaussianNB()
model.fit(xTrain, yTrain)
yPredicted = model.predict(xTest)
#print(yPrediction)
print('Accuracy: ', accuracy_score(yTest, yPredicted))
I am not sure how to include the data that I am using but I am trying to predict the 'size_womenswear'. There are 8 different sizes that I have encoded to predict and I have moved this column to the end of the dataframe. so y is the dependent and x are the independent (all the other columns)
I am using a Gaussian Naive Bayes classifier to try and classify the 8 different sizes and then test on 25% of the data. The results are not very good.
I don't know why I am only getting an accuracy of 61% when I am working with 80,000 rows. I am very new to Machine Learning and would appreciate any assistance. Is there a better method that I could use in this case than Gaussian Naive Bayes?
can't comment, just throwing out some ideas;
Maybe you need to deal with class imbalance, and try other model that will fit the data better? try the xgboost or lightgbm package given good data they usually perform pretty good in general, but it really depends on the data.
Also the way you split train and test, does the resulting train and test data set has similar distribution for your Y? that's very important.
Last thing, for classification models the performance measurement can be a bit tricky, try some other measurement methods. F1 scores or try to draw a confusion matrix and see what your predictions vs Y looks like. perhaps your model is predicting everything to one
or just a few classes.
I'm new to machine learning and trying Sklearn for the first time. I have two dataframes, one with data to train a logistic regression model (with 10-fold cross-validation) and another one to predict classes ('0,1') using that model.
Here's my code so far using bits of tutorials I found on Sklearn docs and on the Web:
import pandas as pd
import numpy as np
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.preprocessing import normalize
from sklearn.preprocessing import scale
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn import metrics
# Import dataframe with training data
df = pd.read_csv('summary_44.csv')
cols = df.columns.drop('num_class') # Data to use (num_class is the column with the classes)
# Import dataframe with data to predict
df_pred = pd.read_csv('new_predictions.csv')
# Scores
df_data = df.ix[:,:-1].values
# Target
df_target = df.ix[:,-1].values
# Values to predict
df_test = df_pred.ix[:,:-1].values
# Scores' names
df_data_names = cols.values
# Scaling
X, X_pred, y = scale(df_data), scale(df_test), df_target
# Define number of folds
kf = KFold(n_splits=10)
kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator
# Logistic regression normalizing variables
LogReg = LogisticRegression()
# 10-fold cross-validation
scores = [LogReg.fit(X[train], y[train]).score(X[test], y[test]) for train, test in kf.split(X)]
print scores
# Predict new
novel = LogReg.predict(X_pred)
Is this the correct way to implement a Logistic Regression?
I know that the fit() method should be used after cross-validation in order to train the model and use it for predictions. However, since I called fit() inside a list comprehension I really don't know if my model was "fitted" and can be used to make predictions.
I general things are okay, but there are some problems.
Scaling
X, X_pred, y = scale(df_data), scale(df_test), df_target
You scale training and test data independently, which isn't correct. Both datasets must be scaled with the same scaler. "Scale" is a simple function, but it is better to use something else, for example StandardScaler.
scaler = StandardScaler()
scaler.fit(df_data)
X = scaler.transform(df_data)
X_pred = scaler.transform(df_test)
Cross-validation and predicting.
How your code works? You split data 10 times into train and hold-out set; 10 times fit model on train set and calculate score on hold-out set. This way you get cross-validation scores, but the model is fitted only on a part of data. So it would be better to fit model on the whole dataset and then make a prediction:
LogReg.fit(X, y)
novel = LogReg.predict(X_pred)
I want to notice that there are advanced technics like stacking and boosting, but if you learn using sklearn, then it is better to stick to the basics.
I've just split my data into a training and testing set and my plan is to train a Linear Regression model and be able to check what the performance is like using my testing split.
My current code is:
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
df = pd.read_csv('C:/Dataset.csv')
df['split'] = np.random.randn(df.shape[0], 1)
split = np.random.rand(len(df)) <= 0.75
training_set = df[split]
testing_set = df[~split]
Is there a proper method I should be using to plot a Linear Regression model from an external file such as a .csv?
Since you want to use scikit-learn, here's an approach using sklearn.linear_model.LinearRegression:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_train, y_train = training_set[x_vars], training_set[y_var]
X_test, y_test = testing_test[x_vars], testing_test[y_var]
model.fit(X_train, y_train)
predictions = model.predict(X_test)
Depending on whether you need more descriptive output, you might also look into use statsmodels for linear regression.