confusion-matrix-180121133502.pptx
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A confusion matrix is a table that is used to evaluate the performance of a classification model. It displays the number of true positives, true negatives, false positives, and false negatives. The confusion matrix shows how often the model correctly and incorrectly predicts samples for each class. In the example confusion matrix, the model correctly classified 50 apples and 50 oranges, but incorrectly classified 15 cases as apples and 10 cases as oranges.
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UNetEliyaLaialy (2).pptx
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
U-Netpresentation.pptx
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
ImageSegmentation (1).ppt
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more.
Key points about image segmentation:
Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding.
Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image.
Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image.
Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs).
Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task.
Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles.
Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth.
Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation.
Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features.
Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception.
Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
SVM (2).ppt
Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification and regression tasks. It works by finding a hyperplane in a high-dimensional space that best separates data points of different classes. SVM aims to maximize the margin between the classes, where the margin is defined as the distance between the hyperplane and the nearest data points from each class. The data points that are closest to the hyperplane are called support vectors.
Here are some key concepts associated with SVM:
Hyperplane: In a two-dimensional space, a hyperplane is a line that separates the data points of different classes. In higher dimensions, it becomes a hyperplane. SVM tries to find the hyperplane with the maximum margin between classes.
Margin: The margin is the distance between the hyperplane and the nearest data points of each class. SVM seeks to maximize this margin.
Support Vectors: These are the data points that are closest to the hyperplane and have the most influence on determining its position. These points "support" the placement of the hyperplane.
Kernel Trick: SVM can be extended to non-linearly separable data using the kernel trick. A kernel function takes the original feature space and maps it to a higher-dimensional space where the data might be linearly separable. Common kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel.
C parameter: In SVM, the C parameter is a regularization parameter that balances the trade-off between maximizing the margin and minimizing the classification error. A small C value allows for a larger margin but may lead to more misclassifications, while a large C value prioritizes correct classification over margin maximization.
SVM can be used for both classification and regression tasks:
Classification: In classification, SVM tries to find a hyperplane that separates data points into different classes. New data points can then be classified based on which side of the hyperplane they fall.
Regression: In regression, SVM is used to find a hyperplane that best fits the data points. The goal is to minimize the error between the actual and predicted values.
SVMs have been widely used in various fields such as image classification, text categorization, bioinformatics, and more. However, they can be sensitive to the choice of hyperparameters and might not perform well on extremely noisy or overlapping data.
It's important to note that while SVMs are a powerful and versatile algorithm, newer algorithms like deep learning models have gained popularity due to their ability to automatically learn complex features and patterns from data.
Support Vector Machine (SVM) is a powerful machine learning algorithm for classification and regression. It finds a hyperplane that best separates data into classes, aiming to maximize the margin between them. Support vectors, the closest data points to the hyperplane, influence its position.
2024 State of Marketing Report – by Hubspot
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
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ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
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UNetEliyaLaialy (2).pptx
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
U-Netpresentation.pptx
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
ImageSegmentation (1).ppt
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more.
Key points about image segmentation:
Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding.
Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image.
Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image.
Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs).
Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task.
Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles.
Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth.
Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation.
Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features.
Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception.
Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
SVM (2).ppt
Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification and regression tasks. It works by finding a hyperplane in a high-dimensional space that best separates data points of different classes. SVM aims to maximize the margin between the classes, where the margin is defined as the distance between the hyperplane and the nearest data points from each class. The data points that are closest to the hyperplane are called support vectors.
Here are some key concepts associated with SVM:
Hyperplane: In a two-dimensional space, a hyperplane is a line that separates the data points of different classes. In higher dimensions, it becomes a hyperplane. SVM tries to find the hyperplane with the maximum margin between classes.
Margin: The margin is the distance between the hyperplane and the nearest data points of each class. SVM seeks to maximize this margin.
Support Vectors: These are the data points that are closest to the hyperplane and have the most influence on determining its position. These points "support" the placement of the hyperplane.
Kernel Trick: SVM can be extended to non-linearly separable data using the kernel trick. A kernel function takes the original feature space and maps it to a higher-dimensional space where the data might be linearly separable. Common kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel.
C parameter: In SVM, the C parameter is a regularization parameter that balances the trade-off between maximizing the margin and minimizing the classification error. A small C value allows for a larger margin but may lead to more misclassifications, while a large C value prioritizes correct classification over margin maximization.
SVM can be used for both classification and regression tasks:
Classification: In classification, SVM tries to find a hyperplane that separates data points into different classes. New data points can then be classified based on which side of the hyperplane they fall.
Regression: In regression, SVM is used to find a hyperplane that best fits the data points. The goal is to minimize the error between the actual and predicted values.
SVMs have been widely used in various fields such as image classification, text categorization, bioinformatics, and more. However, they can be sensitive to the choice of hyperparameters and might not perform well on extremely noisy or overlapping data.
It's important to note that while SVMs are a powerful and versatile algorithm, newer algorithms like deep learning models have gained popularity due to their ability to automatically learn complex features and patterns from data.
Support Vector Machine (SVM) is a powerful machine learning algorithm for classification and regression. It finds a hyperplane that best separates data into classes, aiming to maximize the margin between them. Support vectors, the closest data points to the hyperplane, influence its position.
2024 State of Marketing Report – by Hubspot
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
Everything You Need To Know About ChatGPT
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
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Cosmetic shop management system project report.pdf
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
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Thank me later.
samsarthak31@gmail.com
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Automobile Management System Project Report.pdf
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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4:这个认证书并且可以归档倒地方
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdf
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Courier management system project report.pdf
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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Thank me later.
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学校原件一模一样【微信：741003700 】《(ANU毕业证书)澳洲国立大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
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- 1. Confusion Matrix Explained Samuel Bohman
- 2. What is a Confusion Matrix? A common method for describing the performance of a classification model consisting of true positives, true negatives, false positives, and false negatives. It is called a confusion matrix because it shows how confused the model is between the classes.
- 3. True Positives Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0 The model correctly classified 50 apples and 50 oranges.
- 4. True Negatives for Apple The model correctly classified 75 cases as not belonging to class apple. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 5. True Negatives for Orange The model correctly classified 105 cases as not belonging to class orange. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 6. True Negatives for Pear The model correctly classified 115 cases as not belonging to class pear. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 7. False Positives for Apple The model incorrectly classified 15 cases as apples. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 8. False Positives for Orange The model incorrectly classified 10 cases as oranges. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 9. False Positives for Pear The model incorrectly classified 70 cases as pears. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 10. False Negatives for Apple The model incorrectly classified 55 cases as not belonging to class apple. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 11. False Negatives for Orange The model incorrectly classified 30 cases as not belonging to class orange. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
- 12. False Negatives for Pear The model incorrectly classified 10 cases as not belonging to class pears. Predicted class Actual class Apple Orange Pear Apple 50 5 50 Orange 10 50 20 Pear 5 5 0