Develop Reference

Machine Learning Using Scikit-Learn & SVM

Load popular digits dataset from sklearn.datasets module and assign it to variable digits.
Split digits.data into two sets names X_train and X_test. Also, split digits.target into two sets Y_train and Y_test.
Hint: Use train_test_split() method from sklearn.model_selection; set random_state to 30; and perform stratified sampling.
Build an SVM classifier from X_train set and Y_train labels, with default parameters. Name the model as svm_clf.
Evaluate the model accuracy on the testing data set and print its score.
I used the following code:
import sklearn.datasets as datasets
import sklearn.model_selection as ms
from sklearn.model_selection import train_test_split
digits = datasets.load_digits();
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30)
print(X_train.shape)
print(X_test.shape)
from sklearn.svm import SVC
svm_clf = SVC().fit(X_train, y_train)
print(svm_clf.score(X_test,y_test))
I got the below output.
(1347,64)
(450,64)
0.4088888888888889
But I am not able to pass the test. Can someone help with what is wrong?

You are missing the stratified sampling requirement; modify your split to include it:
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30, stratify=y)
Check the documentation.

How do I properly fit a sci-kit learn model using a pandas dataframe?

I am trying to create a machine learning program in sci-kit learn. I am using a CSV file to store data, and have decided to use Pandas data frame to import and format this data. I cannot figure out how to fit this data frame with the model.
My CSV file has one feature, age, and one target, weight. I am using a linear regression algorithm to predict the weight using the age. I do realize this isn't the best algorithm to use with this data.
When I run this code I get the error "ValueError: Found input variables with inconsistent numbers of samples: [10, 40]"
Here is my code:
# Imports
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# Load And Split Data
data = pd.read_csv("awd.csv")
feature_cols = ['Ages']
X = data.loc[:, feature_cols]
y = data.loc[:, "Weights"]
X_train, y_train, X_test, y_test = train_test_split(X, y, random_state=0, train_size=0.2)
# Train Model
lr = LinearRegression()
lr.fit(X_train, y_train)
# Scores
print(f"Test set score: {round(lr.score(X_test, y_test), 3)}")
print(f"Training set score: {round(lr.score(X_train, y_train), 3)}")
The first 5 lines of my CSV file:
Ages,Weights
1,19
1,21
2,26
2,32

You're assigning the return values incorrectly. See below:
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.2)

You should correct the order of X_train, X_test, y_train and y_test like this:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
See the relevant documentation for details.

StandardScaling ruins the test data score for my linear regression

When I apply StandardScaler to my train data after traintestsplit, the score for the train data is ok but the score for my test data makes no sense.
I tried LinearRegression(normalize=True) and it also made the score of my test data go crazy.
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
ss = StandardScaler()
X_train_sc = ss.fit_transform(X_train)
X_test_sc = ss.transform(X_test)
lr = LinearRegression()
lr.fit(X_train_sc, y_train)
print(lr.score(X_train_sc, y_train))
print(lr.score(X_test_sc, y_test))
Results are:
0.961269156232134
-1.5466488732709964e+19
Why??? Please, help!
Note: If I do not run the StandardScaler, then both my scores make perfect sense.

I am trying to use ensemble learner method sklearn but having model fit issue, getting an value error: too many values to unpack (expected 2)

This is my code, I have those models 1 to 4 run in above cells without any errors.
I will also show my test train split.
Train test split & smote
Error image
from sklearn.ensemble import VotingClassifier
estimators=[('lr', model1,('RF1', model2), ('nn', model3),('RF2',model4))]
model5 = VotingClassifier(estimators, voting='hard')
model5.fit(X_train_nns,y_train_nns)
ypred_en=model5.predict(X_test)
model5.score(X_test, y_test)
This is how i split & over sampled it
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10,
random_state=42)
from imblearn.over_sampling import SMOTE
sm = SMOTE()
X_train_nns, y_train_nns = sm.fit_sample(X_train, y_train.values.ravel())
Can anyone tell me what i am doing wrong here.

Why I am getting this value error in KNN model?

I am applying KNN model on breast cancer wisconsin data but everytime I run the code I get this error:
ValueError: Found input variables with inconsistent numbers of samples: [559, 140]
import numpy as np
import pandas as pd
from sklearn import preprocessing,cross_validation,neighbors
df=pd.read_csv('breast-cancer-wisconsin.data.txt')
df.replace('?',-99999,inplace=True)
df.drop(['id'],1,inplace=True)
X=np.array(df.drop(['class'],1))
y=np.array(df['class'])
X_train, y_train, X_test, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
clf = neighbors.KNeighborsClassifier()
clf.fit(X_train, y_train)
accuracy=clf.score(X_test, y_test)
print(accuracy)
example=np.array([4,2,1,1,1,2,3,2,1])
example=example.reshape(-1,1)
prediction=clf.predict(example)
print(prediction)

The output of cross_validation.train_test_split, as per the documentation, should be X_train, X_test, y_train, y_test. Change that line in your code to:
X_train,X_test,y_train,y_test=cross_validation.train_test_split(X,y,test_size=0.2)