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This is my code.
names = []
res = []
for name, model in models:
kfold = StratifiedKFold(n_splits=10, random_state=None)
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
results.append(cv_results)
names.append(name)
res.append(cv_results.mean())
print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
pyplot.ylim(.990, .999)
pyplot.bar(names, res, color ='maroon', width = 0.6)
pyplot.title('Algorithm Comparison')
pyplot.show()
This error occurred when I executed the code.
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Which one is more correct or there is any other way to scale data? (I've used StandardScaler as an example)
I've tried every way and computed the accuracy of every model but there is no meaningful difference but I want to know which way is more correct
dataset= pd.read_csv("wine.csv")
x = dataset.iloc[:,:13]
y = dataset.iloc[:,13]
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.8,random_state=0)
sc=StandardScaler()
x_train=sc.fit_transform(x_train)
x_test=sc.fit_transform(x_test)
or
dataset= pd.read_csv("wine.csv")
x = dataset.iloc[:,:13]
y = dataset.iloc[:,13]
sc=StandardScaler()
x = sc.fit_transform(x)
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.8,random_state=0)
or
dataset= pd.read_csv("wine.csv")
x = dataset.iloc[:,:13]
y = dataset.iloc[:,13]
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.8,random_state=0)
sc=StandardScaler()
x_train=sc.fit_transform(x_train)
x_test=sc.transform(x_test)
Test data should not bee seen or used during the training a of model as they are used to assert the performance of the model.
Therefore the last option is the correct one. The scaling parameter should be computed solely on the train set as follow:
sc=StandardScaler()
x_train=sc.fit_transform(x_train)
x_test=sc.transform(x_test)
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Learning was carried out using the random forest algorithm. I want to append the results of the input data to the existing input data, how do I do it? In the case of scikit-learn, it provides model evaluation criteria such as accuracy, precision, recall, and f1 score for the result, but I am not sure if there is a function that returns the label of the prediction result like keras. I don't know where to start with the code, so I'll just ask a question.
Usually, you have something like this when using sklearn:
input_data = pd.read_csv("path/to/data")
features = ["area", "location", "rooms"]
y = input_data["Price"]
X = input_data[features]
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
model = RandomForest()
model.fit(train_X, train_y)
Now your model is trained. As you mentioned you could get different metrics from the model using sklearn on your validation set.
Getting output label from the model means getting predictions(inference):
output_label = model.predict(val_X)
#This is an nd array with the size of val_y
x = pd.DataFrame(val_X,columns=["input"])
x["output_label"] = output_label
Or you could use numpy.concatenate to append the labels directly to your input data
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after spliting x_train ,y_train .
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)
print(x_train.shape,y_train.shape)
(354, 13) (354,)
Again I need to join ytrain column to xtrain .Price is new column
x_train['Price']=y_train but this does not work
I am trying to use iloc like following but it gives warning
x_train['price']=y_train.iloc[0:354]
please help me out regarding this
You get that warning because x_train is a view of X. Using an example:
df = pd.DataFrame(np.random.uniform(0,1,(100,4)),
columns=['x1','x2','x3','y'])
X = df[['x1','x2','x3']]
Y = df[['y']]
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)
You can see:
x_train._is_view
True
If I try to run your code, I get the same warning.
See this post about views of a data frame and also this on dealing with the warning. What you can do make a copy if you don't think it's an issue:
x_train = x_train.copy()
x_train['Price'] = y_train
Or use insert:
x_train.insert(x_train.shape[1],"Price",y_train)
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I'm trying to use GridSearchCV for SVM linear but I get this error:
AttributeError: 'SVC' object has no attribute 'best_estimator_'
the code of the SVM linear:
classifier = SVC()
classifier = GridSearchCV(classifier, {'C':[0.001, 0.01, 0.1, 1, 10,0.1, 100, 1000]}, cv=3, n_jobs=4)
classifier = SVC(kernel='linear')
classifier.fit(train_vectors, train_labels)
classifier = classifier.best_estimator_
can anyone help?
Do this:
classifier = SVC(kernel='linear')
gridsearch = GridSearchCV(classifier, {'C':[0.001, 0.01, 0.1, 1, 10,0.1, 100, 1000]}, cv=3, n_jobs=4)
gridsearch.fit(train_vectors, train_labels)
best_params = gridsearch.best_params_
classifier = gridsearch.best_estimator_
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I am performing classification by LogisticRegression over a large dataset (1.5 million observations) using LeaveOneGroupOut cross-validation. I am using scikit-learn for implementation. My code takes around 2 days to run and I would appreciate your inputs on how to make it faster. A snippet of my code is shown below:
grp = data['id_x'].values
logo = LeaveOneGroupOut()
LogReg = LogisticRegression()
params_grid = {'C': [0.78287388, 1.19946909, 1.0565957 , 0.69874106, 0.88427995, 1.33028731, 0.51466415, 0.91421747, 1.25318725, 0.82665192, 1, 10],
'penalty': ['l1', 'l2'] }
random_search = RandomizedSearchCV(LogReg, param_distributions = params_grid, n_iter = 3, cv = logo, scoring = 'accuracy')
random_search.fit(X, y, grp)
print random_search.best_params_
print random_search.best_score_
I am going to make the following assumptions:
1- you are using scikit-learn.
2- you need your code to be faster.
To get your final results faster, you can train multiple models at once by running them in parallel. To do so, you need to modify the variable n_jobs in scikit-learn. Possible options for n_jobs can be #of_CPU_cores or #of_CPU_cores-1 if you are not running anything else on your computer while training the model.
Examples:
RandomizedSearchCV in parallel:
random_search = RandomizedSearchCV(LogReg, n_jobs=3, param_distributions = params_grid, n_iter = 3, cv = logo, scoring = 'accuracy')
LogisticRegression in parallel:
LogisticRegression(n_jobs=3)
I recommend parallelizing only RandomizedSearchCV.
It might be helpful to also look at the original scikit-learn documentations:
sklearn.linear_model.LogisticRegression
sklearn.model_selection.RandomizedSearchCV