CN7023 Artificial Intelligence and Machine Vision
Pokémon Master Identification for binary classification with Machine Learning algorithms
Introduction
Overview
Machine learning is the widely used application that is designed for solving the different problem that is identified over real world application. The major problem is included with the classification of dataset that is either binary or multi classification. This is developed with the machine learning algorithm that is applied with the accurate measure to state the pokemon accuracy. The legendary pokemon is classified with the target application that is served with the machine learning algorithms to predict the powerful target (David Simões, 2020). The pokemon dataset is used for the classification to improve the accuracy and performance that is classified and helped with the different features. Classification refers to the separation of the data that is labeled with the different classes. This is associated with many other applications to observe spam mail or with the label to predict the multi class classification. This mail is named as spam or not but this is predicted with the multi class classification based on the legendary application.
Objective
The main objective of this article is to describe the classification with the binary label that is used to separate the spam email with the dataset in multi class classification (Wu, 2019). The two classes legendary are measured with the different accuracy that is provided with the confusion matrix based on the correct or wrong classification. The image is uploaded and classified with the visual application that labels the confusion matrix.
Simulation
Dataset
The dataset is predicted with the legendary column that is analyzed with the pokemon dataset for the prediction of the label that is encoded and decoded with the dataset. The classification problem is encoded with the pokemon legendary that includes the true or false statement that might step to either 0 or 1. The label encoding is used with the encode terms like true as 1 and false as 0 that is required with the dataset processing step. This final dataset is provided with the machine learning algorithm that is processed with the encoding techniques that is available with the different algorithm (Liapis, 2018). There are 8 column used with the various features that is analyzed with the target variable that is accessed with the predicted classification.
Exploratory Analysis is carried with the water pokemon that is very common and compared with the various factors that is analyzed with the pokemon dataset (Dan Huang, 2019). This is found with the multiple machine learning technique that is analyzed based on the correlated variables that is accessed with the pokemon dataset which offers the highest value of correlation that is addressed over special attack. Logistic regression is widely accessed with the binary classification that is used with the logit function that is generated over the outcome. This output is generated as 0 or 1 that is analyzed with the sigmoid function. The sigmoid application or function is analyzed with the activation function that is accessed over the sigmoid function.
Neural Network
Neural network is the network that is analyzed with the human brain and this is 32 layered approaches which is hidden and provided with the different dimensions. This is analyzed with the 8 feature that is classified with the binary classification problem which is activated with the machine learning approach (Hiroyuki Ihara, 2018). This neural network is used with the analysis of the performance over the accuracy of 90.62% that is classified over the machine learning classification approach. The pokemon dataset is explored with the various abilities that is used for mapping the dataset and combat dataset that is offered over various abilities. The missing data is handled with the several spot that is categorized over the spot empty data and numerical value is analyzed with the pokemon dataset.
Gaussian Naïve Bayes
The dataset is processed with the reduced dataset that is filled with the categorical order which is categorized over not applicable analysis (Melissa M. Chow, 2020). This is analyzed with the reduced dataset that is processed with the maximum and medium value that is addressed with the creation of dataset that is misplaced with the dataset. The numerical value is determined with the various work model or number that is converted with the numerical value from the categorical value that may be encoded or decoded with the hasher that is encoded with the converted application (Devi Acharya, 2019). There are two different types of numerical value that is provided with the type 1 and type 2 that is created with the one hot encoding technique. This is solved with the feature hash value that is encoded with the very large dataset that is concatenated with the encoding process.
Support Vector Machine
Support vector machine is the machine learning algorithm that is solved with the classification problem that is identified with the separate classes. This algorithm is analyzed with the regression and classification analysis that is provided with the different set of trained dataset (Joseph Flaherty, 2021). This trained dataset is helped with the build model that is accessed over the classification of binary value in pokemon dataset. This hyper plane is separated with the two different classes that is produced with the better result based on the trained larger dataset. This algorithm is presented with the slower process that is scored with the achieved application. This algorithm is measured with the dataset that is offered with the accurate dataset that is produced with the better result based on the multiple machine learning dataset. I mapped with the combat dataset that is created with the trained dataset that is predicted over various column and trained data.
Logistic Regression
Logistic regression is analyzed with the conversion of the different dataset that is predicted with the dependent variable either 0 or 1 that is represented over different outcome. This decision tree is classified with the illustrated application is processed with the regression type depicts the provided outcome (Xinyue Zhang, 2019). This model is build with the possible outcome that is depicted with the learning practice that is accessed with the improved speech recognition through different genetics. This requires the problem that is predicted with the machine learning problem dataset. The first trained dataset is characterisized with the different species that is learnt with the legendary pokemon. This trained dataset is sorted with the separated file that is tested with the predicted model that is specified over the dataset to classify the binary classification.
K Nearest Neighbor
KNN is the nearest algorithm that is used with the classification problem that is assigned with the nearest value that is denoted with the available model. This is analyzed with the trained dataset that access the neighbor to increase the accuracy with the prediction of classification technique (Samuel da Silva Oliveira, 2020). Voting is happened with the KNN algorithm that is pointed with the available model that secures the achieved accuracy with the algorithm with the overall percentage. Clustering algorithm is comprised with the interconnected layers that are processed technique that is experienced with the non linear relationship. This is helped with the variable that is assigned with the group that is provided with the series of interconnected layers that is modeled with the dimensional data.
Random Forest Algorithm
Random forest algorithm is accompanied with the different process that is provided with the best result to predict the algorithm. This algorithm is started with the generated decision tree model that is analyzed with the data point that is classified over the groups (Kent T. Jacobs, 2020). The correlation is analyzed with the various components that are helped with the use of positive and negative numbers to correlate the legendary features. This feature is orrelated with the attack and defense that is useful with the legendary action. This is followed with the dragon type that is predicted with the legendary action that is calculated with the transformed dataset.
Result
Neural network is the technique that is predicted with the validated result that is represented with the X and Y value. This is tested and validated with the exploratory analysis that is validated with the pokemon dataset. Pokemon dataset is provided with the different features that are found with the random forest algorithm that is followed over the neural network algorithm in order to offer better result (Samuli Laato, 2021). The better result is applied with the hidden layer with the disaster pokemon that is accessed with the different process. This is gradient boosted decision making process that is performed with the achieved accuracy that is classified over the specified system. This is specified with the decision tree algorithm that is fixed with the random forest algorithm.
The result is measured with the trained dataset that includes the 97.15% that is represented with the accuracy and epoch that is identified over neural network algorithm. This is presented with the trained and split data that is accessed with the trained test dataset. The data is trained and analyzed with the different model that is predicted with the array of data over the model that is described with the system (Nath, 2018). This is presented over X and Y axis that is trained with the dataset that is accessed with the random basis. This hyper parameter is provided with the different machine learning algorithm that is defined with the special attack and defense to be predicted with the battle. Pokemon model is predicted with the different battle that might be presented with the combat dataset.
The accuracy is presented with the machine learning technique that is presented with the graphical representation. This is provided in general with the machine learning and artificial intelligence that is presented or implemented with the MATLAB is analyzed with the problem solving technique and this is offered with the simple process that is presented with the straight forward approach (Llobet Sanchez, 2018). This is the automated process that is enhanced over the decision making process which is explicitly programmed with the machine learning techniques. Data is collected with the various sources that are stored with the database that is optimized with the business process application. This learning process is aligned with the business process to enhance the business process that is sorted with the machine learning algorithm.
Critical Analysis
The accuracy is measured with the 97.15% that is pointed with the incorrect category to be scaled. The business process is automated with the small quantity of data that is required with the machine learning algorithm. There is specific learning process that is ensemble with the machine learning system. Neural network algorithm is presented with the decision tree making approach that is specified over the specified variable is presented with the analysis and classification of data. The result is calculated with the specific problem that includes the measure of accuracy such as number of epoch identification. The accuracy is measured with the number of epoch that is connected with the K nearest algorithm. This is provided with the smote process that is defined with the number line to analyze and distribute the skewed distribution. Two columns have different category that is analyzed with the hot encoding process that is checked with same.
Confusion matrix is the table that is described with the performance matrix that is classified with the classification model which is provided with the test data. The data is validated with the true values that are provided with the related terminology that is provided with the variable application. This is related with the visualization of the data that is predictive and analyzed with the recall and precision that is compared with the true positive, true negative, false positive and false negative that is presented over the confusion matrix. The max value is required with the data modeling that is presented with the unrealistic process that offers the negative values. The left and right skewed distribution is provided with the positive and negative distribution that is learned over the peak that is analyzed with the pokemon dataset. This is presented over the dataset that is directed towards the positive and negative direction that is classified over the peak in right or left peak. This value is presented with the two different columns which is solved with the different issues that is applied with the categorical binary data that is presented with the high dimensions and redundancy.
Conclusion
Regression algorithm is classified with the different categories of the data that is represented with the legendary and non legendary pokemon that is analyzed with 0 or 1. This is calculated with the numerical value which is predicted over the real value of the houses that is performed over the mean average error and overall performance is measured with the classification result. This is not trivial that is considered to be more classification that is categorized with the pokemon data analysis. Accuracy is predicted with the dataset that is analyzed with the sample dataset that is built with the machine learning algorithm with the different classes. Pokemon master is pre built with the expensive master ball that is accessed with the despite analysis that is frustrated over the machine learning algorithm. The accuracy is measured with the different variable that is presented with the actual label predicted over actual label. This is measured with the 97.15% accuracy with the epoch that is labeled with the data points. Each row is represented with the predicted dataset that offers the real world application that is transposed over the interpreted application. The matrix is developed with the TP, FP, TN and FN based on the requirements over various insight and outsights.
References
Dan Huang, S. L., 2019. A Self-Play Policy Optimization Approach to Battling Pokémon. https://ieeexplore.ieee.org/abstract/document/8848014/, pp. 1-4.
David Simões, S. R. N. L. L. P. R., 2020. Competitive Deep Reinforcement Learning over a Pokémon Battling Simulator. https://ieeexplore.ieee.org/abstract/document/9096092/, pp. 40-45.
Devi Acharya, R. A. B. O. M. S., 2019. Gotta Generate ’em All! Pokemon (With Deep Learning). https://creativecoding.soe.ucsc.edu/courses/cmpm202_w19/projects/GottaGenerateEmAll_ACM_Project3.pdf.
Hiroyuki Ihara, S. I. S. O. M. K., 2018. Implementation and Evaluation of Information Set Monte Carlo Tree Search for Pokémon. https://ieeexplore.ieee.org/abstract/document/8616371, pp. 2182-2187.
Joseph Flaherty, A. J. B. A., 2021. Playing Pokemon Red with Reinforcement Learning. https://scholarworks.calstate.edu/downloads/vq27zt20r.
Kent T. Jacobs, S. W. M., 2020. OpenStreetMap quality assessment using unsupervised machine learning methods. https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12680, pp. 1280-1298.
Liapis, A., 2018. Recomposing the Pokémon Color Palette. https://link.springer.com/chapter/10.1007/978-3-319-77538-8_22, pp. 308-324.
Llobet Sanchez, M., 2018. Learning complex games through self play – Pokémon battles. https://upcommons.upc.edu/handle/2117/121655.
Melissa M. Chow, L. T. K. K., 2020. Who’s That Pokemon: Pokedex Project. https://scholarlycommons.pacific.edu/purcc/2020/events/55/.
Nath, S., 2018. Understanding the rise of augmented reality–based apps post-Pokémon GO. https://www.ingentaconnect.com/content/intellect/iscc/2018/00000009/00000003/art00005, pp. 319-334.
Samuel da Silva Oliveira, G. E. P. L. S. A. C. G. C. A. S. B. A. M. P. C. B. M. C., 2020. Team Recommendation for the Pokémon GO Game Using Optimization Approaches. https://www.sbgames.org/proceedings2020/ComputacaoFull/208698.pdf.
Samuli Laato, S. R., 2021. When Player Communities Revolt Against the Developer: A Study of Pokémon GO and Diablo Immortal. https://link.springer.com/chapter/10.1007/978-3-030-91983-2_15, pp. 194-201.
Wu, M.-H., 2019. The applications and effects of learning English through augmented reality: a case study of Pokémon Go. https://www.tandfonline.com/doi/abs/10.1080/09588221.2019.1642211, pp. 778-812.
Xinyue Zhang, J. W. Y. L. R. J. M. P. Z. H., 2019. Catching All Pokémon: Virtual Reward Optimization With Tensor Voting Based Trajectory Privacy. https://ieeexplore.ieee.org/abstract/document/8542686, pp. 883-892.
………………………………………………………………………………………………………………………..
Know more about UniqueSubmission’s other writing services:
Essay Writing Help
Dissertation Writing Help
Case Studies Writing Help
MYOB Perdisco Assignment Help
Presentation Assignment Help
Proofreading & Editing Help
