Machine Learning 简明教程

Machine Learning - Data Visualization

数据可视化是机器学习 (ML) 的一个重要方面，因为它有助于分析和传达数据中的模式、趋势和见解。数据可视化涉及创建数据的图表表示，这有助于识别可能无法从原始数据中明显看出模式和关系。

Data visualization is an important aspect of machine learning (ML) as it helps to analyze and communicate patterns, trends, and insights in the data. Data visualization involves creating graphical representations of the data, which can help to identify patterns and relationships that may not be apparent from the raw data.

以下是数据可视化在机器学习中使用的一些方式 −

Here are some of the ways data visualization is used in machine learning −

  1. Exploring Data − Data visualization is an essential tool for exploring and understanding data. Visualization can help to identify patterns, correlations, and outliers, and can also help to detect data quality issues such as missing values and inconsistencies.

  2. Feature Selection − Data visualization can help to select relevant features for the ML model. By visualizing the data and its relationship with the target variable, you can identify features that are strongly correlated with the target variable and exclude irrelevant features that have little predictive power.

  3. Model Evaluation − Data visualization can be used to evaluate the performance of the ML model. Visualization techniques such as ROC curves, precision-recall curves, and confusion matrices can help to understand the accuracy, precision, recall, and F1 score of the model.

  4. Communicating Insights − Data visualization is an effective way to communicate insights and results to stakeholders who may not have a technical background. Visualizations such as scatter plots, line charts, and bar charts can help to convey complex information in an easily understandable format.

用于 Python 中数据可视化的流行库包括 Matplotlib、Seaborn、Plotly 和 Bokeh。这些库提供了广泛的可视化技术和自定义选项，以满足不同的需求与偏好。

Some popular libraries used for data visualization in Python include Matplotlib, Seaborn, Plotly, and Bokeh. These libraries provide a wide range of visualization techniques and customization options to suit different needs and preferences.

data visualization techniques

Univariate Plots: Understanding Attributes Independently

最简单的可视化类型是单变量或“单变量”可视化。借助单变量可视化，我们可以独立了解数据集的每个属性。以下是 Python 中实现单变量可视化的某些技术 -

The simplest type of visualization is single-variable or “univariate” visualization. With the help of univariate visualization, we can understand each attribute of our dataset independently. The following are some techniques in Python to implement univariate visualization −

  1. Histograms

  2. Density Plots

  3. Box and Whisker Plots

Multivariate Plots: Interaction Among Multiple Variables

另一种可视化类型是多变量或“多变量”可视化。借助多变量可视化，我们可以了解数据集中多个属性之间的交互。以下是 Python 中用于实现多变量可视化的某些技术 -

Another type of visualization is multi-variable or “multivariate” visualization. With the help of multivariate visualization, we can understand interaction between multiple attributes of our dataset. The following are some techniques in Python to implement multivariate visualization −

  1. Correlation Matrix Plot

  2. Scatter Matrix Plot

在接下来的几章中，我们将着眼于机器学习中可用的一些流行的且被广泛使用的可视化技术。

In the next few chapters, we will look at some of the popular and widely used visualization techniques available in machine learning.