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[ML Tutorials #3] Four keys to create supervised learning model 2498

Tima EL, 1 y.
1 min read.

[ML Tutorials #3] Four keys to create supervised learning model

Tima EL, 2 d.
min read.
Supervised learning is a strategy in machine learning that enables a model to learn from data without being explicitly programmed. In other words, in supervised learning, the model tries to find the relationship between the "input" X and the "output" Y. Therefore, the first key to creating a supervised learning model is the dataset. **Key 1 : Dataset** Having a labeled dataset is essential, including two important types of information: the target variable Y, which is what we want to predict, and the explanatory variable X, which are the factors that help us make predictions. Let's take an example: imagine we want our model to predict the weather (Y) based on factors like temperature, humidity, and wind speed (X). To do this, we gather a dataset with information from the past, where we already know both the weather outcomes (Y) and the corresponding factors (X). This dataset acts like a box of puzzle pieces. Each piece represents one of the factors, and finding the relationship between these pieces defines the weather. We can represent this relationship as a mathematical equation, like this: Y = F(X), where F represents our model. Therefore, the second key is the Model. **Key 2: Model** The fundamental model in supervised machine learning is a linear model expressed as y = ax + b. However, the real world often presents nonlinear problems. In such cases, we explore non-linear models, such as a polynomial of degree two like y = ax² + bx + c, or even of degree three, and beyond. It's crucial to understand that each model has parameters requiring adjustment during training. Consequently, the two remaining critical components are the cost function and the optimization algorithm. **Key 3 : Cost Function** In machine learning, a cost function, also called a loss or objective function, quantifies the gap between the target and predicted values, signifying the model's error. The aim is to minimize this error to craft the most effective model. **Key 4 : Optimizer** Optimizer forms the core of a machine learning model, representing the strategy to discover parameter values that minimize the cost function. It plays a crucial role in fine-tuning the model for optimal performance.

Four keys to create supervised learning model

Tima EL, 1 y.
min read.
Supervised learning is a strategy in machine learning that enables a model to learn from data without being explicitly programmed. In other words, in supervised learning, the model tries to find the relationship between the "input" X and the "output" Y. Therefore, the first key to creating a supervised learning model is the dataset. **Key 1 : Dataset** Having a labeled dataset is essential, including two important types of information: the target variable Y, which is what we want to predict, and the explanatory variable X, which are the factors that help us make predictions. Let's take an example: imagine we want our model to predict the weather (Y) based on factors like temperature, humidity, and wind speed (X). To do this, we gather a dataset with information from the past, where we already know both the weather outcomes (Y) and the corresponding factors (X). This dataset acts like a box of puzzle pieces. Each piece represents one of the factors, and finding the relationship between these pieces defines the weather. We can represent this relationship as a mathematical equation, like this: Y = F(X), where F represents our model. Therefore, the second key is the Model. **Key 2: Model** The fundamental model in supervised machine learning is a linear model expressed as y = ax + b. However, the real world often presents nonlinear problems. In such cases, we explore non-linear models, such as a polynomial of degree two like y = ax² + bx + c, or even of degree three, and beyond. It's crucial to understand that each model has parameters requiring adjustment during training. Consequently, the two remaining critical components are the cost function and the optimization algorithm. **Key 3 : Cost Function** In machine learning, a cost function, also called a loss or objective function, quantifies the gap between the target and predicted values, signifying the model's error. The aim is to minimize this error to craft the most effective model. **Key 4 : Optimizer** Optimizer forms the core of a machine learning model, representing the strategy to discover parameter values that minimize the cost function. It plays a crucial role in fine-tuning the model for optimal performance.