Eurasian stone-curlew drinking at night

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1x Innovations Photo Prints and Wall Art

Eurasian stone-curlew drinking at night

Haim Mizrachy.

Media ID 38959876

Poster Prints of Eurasian stone-curlew drinking at night

Experience the serene beauty of nature with the Media Storehouse range of poster prints, featuring stunning images by renowned artist Haim Mizrachi for 1x Gallery. This captivating print showcases an Eurasian stone-curlew drinking at night, its majestic form illuminated against the darkness. The subtle play of light and shadow creates a sense of intimacy and tranquility, inviting you to step into the world of this nocturnal bird. The Media Storehouse range is dedicated to preserving iconic images from around the globe, ensuring their beauty and significance are preserved for generations to come. With its unique blend of artistry and conservation, this print embodies the perfect fusion of nature's splendor and artistic expression. Bring a touch of serenity into your home with this breathtaking poster print.

Jigsaw Puzzles of Eurasian stone-curlew drinking at night

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from imblearn.over_sampling import SMOTE # Load the dataset into DataFrame object (df) df = pd.read_csv("your_data.csv") # Splitting data into features and target variable. X = df.drop('target', axis=1) # Features y = df['target'] # Target # Train/Test split of 80% training and 20% test sets. X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2 ,random_state=42) scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform ( X_test ) smote = SMOTE(random_state= 42) # Applying oversampling to the training data X_res, y_res = smote.fit_sample( X_train_scaled ,y_train) df['target'] = y_res from sklearn.metrics import accuracy_score,f1_score,roc_auc_score # Model Training and Evaluation model = your_model() model.fit(X_res,y_res) predictions= model.predict (X_test_scaled ) score = f1_score(y_test,predictions ,average='macro') print("F1 Score:", score)

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