翻译 To ascertain that the improvement of dipIQ is statistically significant, we carry out a two sample T-test (with a 95% confidence) between PLCC values obtained by different models on LIVE [86]. After comparing every possible pairs of OU-BIQA models, the results are summarized in Table V, where a symbol “1” means the row model performs signifi- cantly better than the column model, a symbol “0” means the opposite, and a symbol “-” indicates that the row and column models are statistically indistinguishable. It can be observed that dipIQ is statistically better than dipIQ∗, which is better than all previous OU-BIQA models.

为了确保 dipIQ 的改进在统计上具有显著性，我们在 LIVE 数据集 [86] 上对不同模型得到的 PLCC 值进行了双样本 T 检验（置信度为95%）。在比较了 OU-BIQA 模型的所有可能配对后，结果总结如表 V 所示。其中，“1”表示行模型显著优于列模型，“0”表示相反，而“-”表示行和列模型在统计上无法区分。可以观察到 dipIQ 在统计上优于 dipIQ∗，后者优于所有先前的 OU-BIQA 模型。

相关问题

There is a excel that names Health_Data.csv, two columns of it named cholesterol and BMICat.Using decision Tree in Jupyter notebook to build a predictive model to ascertain if a person with a cholesterol level of 3 is mostly overweight.

Sure, here are the steps to build a decision tree model in Jupyter Notebook to predict if a person with a cholesterol level of 3 is mostly overweight using the Health_Data.csv file:

1. Import necessary libraries:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import graphviz

2. Load the dataset:

data = pd.read_csv('Health_Data.csv')

3. Prepare the data for modeling:

# Select the columns of interest
df = data[['cholesterol', 'BMICat']]

# Convert categorical column to numeric
df['BMICat'] = pd.factorize(df['BMICat'])[0]

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df[['cholesterol']], df['BMICat'], test_size=0.2, random_state=42)

4. Train the decision tree model:

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

5. Make predictions on the test set:

y_pred = model.predict(X_test)

6. Evaluate the model's accuracy:

accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

7. Visualize the decision tree:

dot_data = export_graphviz(model, out_file=None, 
                           feature_names=['cholesterol'],  
                           class_names=['Normal Weight', 'Overweight'],  
                           filled=True, rounded=True,  
                           special_characters=True)  
graph = graphviz.Source(dot_data)  
graph

This will display a decision tree that shows the rules used by the model to predict if a person with a cholesterol level of 3 is mostly overweight.

cholesterol 1 3 3 1 1 2 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 3 2 1 1 1 1 3 3 1 1 3 1 1 1 1 1 1 1BMICat Normal Obese Normal Over Weight Normal Over Weight Obese Obese Over Weight Over Weight Over Weight Normal Normal Obese Over Weight Obese Over Weight Normal Over Weight Normal Obese Over Weight Obese Normal Normal Over Weight Normal Obese Normal Over Weight Obese Normal Normal Over Weight Over Weight Normal Obese Over Weight Over Weight Over Weight Over Weight Normal Using decision Tree in Jupyter notebook, build a predictive model to ascertain if a person with a cholesterol level of 3 is mostly overweight.

To build a predictive model using decision tree in Jupyter notebook, we first need to import the necessary libraries and load the dataset into a pandas dataframe:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

df = pd.read_csv('cholesterol_dataset.csv')

Next, we need to split the dataset into training and testing sets:

X = df.drop('BMICat', axis=1)
y = df['BMICat']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Then, we can fit the decision tree classifier on the training data:

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

After fitting the classifier, we can use it to make predictions on the test data and calculate the accuracy of the model:

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Finally, we can use the classifier to make a prediction for a person with a cholesterol level of 3:

prediction = clf.predict([[1, 3, 3, 1, 1, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 3, 2, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1]])
print("Prediction:", prediction)

The output of the above code will be a prediction for the BMI category of the person with a cholesterol level of 3. If the predicted value is "Over Weight", then it means that the model has predicted that the person is mostly overweight.