How to build Naive Bayes models in Python? Putting the theory behind, let’s build some models in Python. We will start with Gaussian before we make our way to categorical and Bernoulli. But first, let’s import data and libraries. Setup We will use the following: Chess games data fro...
for name, model in models: # fit the model model.fit(X_train, y_train) # evaluate the model yhat = model.predict(X_val) acc = accuracy_score(y_val, yhat) # store the performance scores.append(acc) # report model performance return scores We can then call this function to get the...
That's where the ROUGE score comes into play. It allows the comparison of machine-generated output with human-made references to evaluate the accuracy and quality of the machine's performance.Precision and Recall: The Twin Pillars of the ROUGE Score...
Write a Python script to calculate the metrics: import json from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval # Load ground truth annotations gt_coco = COCO('path_to_ground_truth_annotations.json') # Load predicted bounding boxes in COCO format pred_coco = gt_coco...
metrics import accuracy_score from matplotlib import pyplot # load dataset data = read_csv('pima-indians-diabetes.data.csv', header=None) values = data.values # configure bootstrap n_iterations = 1000 n_size = int(len(data) * 0.50) # run bootstrap stats = list() for i in range...
solution and code sample for language identification using Intel® Extension for PyTorch*, which is a version of the popular PyTorch AI framework optimized for use on Intel® processors, and Intel® Neural Compressor, which is a tool to accelerate AI inference without sacrificing ...
In [7]: # exampleoffitting and evaluating a model on the classification datasetfromsklearn.datasetsimportmake_blobsfromsklearn.linear_modelimportLogisticRegressionfromsklearn.metricsimportaccuracy_score # generate dataset X, y = make_blobs(n_samples=1000, centers=2, n_features=2, random_state=1,...
Callget_model_metrics()to calculate accuracy[3]and bleu[4]score on the validation data. model.get_model_metrics() {'seq2seq_acc': 0.9999, 'bleu': 0.9998} BLEU : (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one...
model_selection import train_test_split from sklearn.metrics import accuracy_score # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] Y = dataset[:,8] # split data into train and test sets X_train, X_test, ...
metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier from matplotlib import pyplot # create dataset X, y = make_classification(n_samples=10000, n_features=20, n_informative=5, n_redundant=15, random_state=1) # split into train test sets X_train, X_test, y_train,...