Kaggle Code: Leaf classification Sklearn Classifier application

which Classifier is should I Choose?

This is one of the most import questions to ask when approaching a machine learning problem. I find it easier to just test them all at once. Here's your favorite Scikit-learn algorithms applied to the leaf data. In [1]:

Import NumPy as NP
import pandas as PD
import Seaborn as SNS
import Matplotlib.pyplot as PLT

def warn (*arg S, **kwargs): Pass
import warnings
Warnings.warn = Warn from

sklearn.preprocessing import Labelencoder From
sklearn.cross_validation import stratifiedshufflesplit

train = Pd.read_csv ('.. /input/train.csv ')
test = Pd.read_csv ('.. /input/test.csv ')

Data PreparationIn [2]:

# Swiss Army knife function to organize the data

def encode (train, test):
    le = Labelencoder (). Fit (train.species) 
  labels = Le.transform (train.species)           # Encode species strings
    classes = List (Le.classes_)                    # Save Column Names for submission
    test_ids = test.id                             # Save test IDs for submission
    
    train = Train.drop ([' species ', ' id '], a Xis=1)  
    test = Test.drop ([' ID '], Axis=1)
    
    return train, labels, test, test_ids, Classes

train, labels, test, Test_ids, classes = Encode (train, test)
Train.head (1)

OUT[2]: margin6

	margin1	margin2	margin3	margin4	margin5		margin7	margin8	margin9	margin10	...	Texture55	texture56	texture57	texture58	texture59	Texture60	texture61	texture62	Texture63	texture64
0	0.007812	0.023438	0.023438	0.003906	0.011719	0.009766	0.027344	0.0	0.001953	0.033203	...	0.007812	0.0	0.00293	0.00293	0.035156	0.0	0.0	0.004883	0.0	0.025391

1 rowsx192 columns stratified train/test Split

Stratification is necessary for this dataset because there are a relatively large number of classes (classes for 990 SA Mples). This would ensure we have all classes represented in both the train and test indices. In [3]:

SSS = stratifiedshufflesplit (labels, ten, test_size=0.2, random_state=23)

for Train_index, Test_index in SSS:
    x_ Train, X_test = Train.values[train_index], Train.values[test_index]
    y_train, y_test = Labels[train_index], labels[ Test_index]

Sklearn Classifier Showdown

Simply Looping through out-of-the box classifiers and printing the results. Obviously, these would perform much better after tuning their hyperparameters, but this gives you a decent ballpark idea. In [4]:

From sklearn.metrics import Accuracy_score, log_loss from sklearn.neighbors import kneighborsclassifier from SKLEARN.SVM Import SVC, Linearsvc, nusvc from Sklearn.tree import decisiontreeclassifier from sklearn.ensemble import Randomforestcla Ssifier, Adaboostclassifier, gradientboostingclassifier from Sklearn.naive_bayes import gaussiannb from Sklearn.discriminant_analysis Import lineardiscriminantanalysis from sklearn.discriminant_analysis Import Quadraticdiscriminantanalysis classifiers = [Kneighborsclassifier (3), SVC (kernel= "RBF", c=0.025, Probability=tru
    e), Nusvc (Probability=true), Decisiontreeclassifier (), Randomforestclassifier (), Adaboostclassifier (),  Gradientboostingclassifier (), GAUSSIANNB (), Lineardiscriminantanalysis (), Quadraticdiscriminantanalysis ()] # Logging for Visual Comparison log_cols=["Classifier", "accuracy", "Log Loss"] log = PD. DataFrame (Columns=log_cols) for CLF in Classifiers:clf.fit (X_train, y_train) name =clf.__class__.__name__ print ("=" *30) print (name) print (' ****results**** ') train_predictions = cl F.predict (x_test) acc = Accuracy_score (y_test, train_predictions) print ("accuracy: {:. 4%}". Format (ACC)) T Rain_predictions = Clf.predict_proba (x_test) ll = Log_loss (y_test, train_predictions) print ("Log loss: {}". Format ( ll)) Log_entry = PD