Shamil Nabiyev shamilnabiyev

https://pypi.org/pypi/seaborn/0.11.2/json

import mlflow
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import StratifiedKFold

import optuna
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import accuracy_score

from sklearn.metrics import precision_recall_fscore_support
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
import mlflow

pandoc --filter pandoc-citeproc --bibliography=test.bib --citeproc -t docx -o test.docsx

Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. J. Mach. Learn. Res., 3, 1157-1182. URL
Tang, J., Alelyani, S., & Liu, H. (2014). Feature Selection for Classification: A Review. Data Classification: Algorithms and Applications. URL
...

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 5))

a = pd.Series(np.random.randint(60, 180, 25))
b = pd.Series(np.random.randint(55, 160, 25))

x_min = min(min(a), min(b))
y_max = max(max(a), max(b))

sns.scatterplot(a, b, ax=ax1)

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
import numpy as np
SEED=42

# Number of trees in random forest
n_estimators = [int(x) for x in range(100,505,100)]
# Number of features to consider at every split
max_features = ['auto', 'sqrt']

import numpy as np
from sklearn.preprocessing import OneHotEncoder

# Load the npz file
data = np.load('data.npz')
X, y = data["x"], data["y"]

print(y.shape)
# Output: (1519,)

	# Source: https://sheldonsebastian94.medium.com/resizing-image-and-bounding-boxes-for-object-detection-7b9d9463125a

	import albumentations
	from PIL import Image
	import numpy as np

	sample_img = Image.open("data/img1.jpg")
	sample_arr = np.asarray(sample_img)

	def resize_image(img_arr, bboxes, h, w):