AI үүсгэсэн Supervised machine learning is a type of artificial intelligence (AI) where the model is trained on a labeled datasetmeaning that the input data is paired with corresponding output labels. The goal is for the model to learn the mapping between inputs and outputsallowing it to make predictions or decisions when given newunseen data.,,Here are the key concepts and steps involved in supervised machine learning:,,Labeled Dataset:,,In supervised learningyou start with a dataset where each example consists of input features and corresponding output labels. The labels represent the desired outcome or target variable.,Input Features:,,Features are the variables or attributes that the model uses to make predictions. They are the characteristics of the data that the model analyzes.,Output Labels:,,Output labels are the target variables or the values that the model aims to predict. The model learns to map input features to these labels during the training process.,Training Phase:,,During the training phasethe model is presented with the labeled dataset. It learns to make predictions by adjusting its internal parameters based on the input-output pairs in the training data.,Supervised Learning Algorithms:,,There are various algorithms used in supervised learningdepending on the nature of the problem. Common algorithms include linear regression for regression problemslogistic regression for binary classificationdecision treessupport vector machinesand neural networks for more complex tasks.,Loss Function:,,The loss function is a measure of how well the model's predictions match the actual labels. During trainingthe model aims to minimize the loss functionadjusting its parameters to improve accuracy.,Validation Set:,,To assess the model's performance during training and avoid overfittinga separate validation set is often used. The model is evaluated on this set to ensure it generalizes well to newunseen data.,Testing Phase:,,After trainingthe model is tested on a separate dataset not used during training or validation. This provides an unbiased evaluation of its performance and generalization capabilities.,Metrics:,,Evaluation metrics depend on the type of problem. Common metrics include accuracyprecisionrecallF1 score for classification problemsand mean squared error or R-squared for regression problems.,Hyperparameter Tuning:,,Fine-tune the model's hyperparameters to optimize its performance. This process may involve adjusting learning ratesregularization parametersor the architecture of more complex models.,Prediction/Inference:,,Once the model is trained and evaluatedit can be used to make predictions on newunseen data by inputting the features and obtaining the predicted output. Текст эффект