standard normalization

简明释义

标准正规化

英英释义

例句

1.The performance of neural networks can be significantly enhanced by applying standard normalization 标准归一化 to the input data.

通过对输入数据应用标准归一化 standard normalization，可以显著提高神经网络的性能。

2.In our analysis, we found that standard normalization 标准归一化 reduced the impact of outliers.

在我们的分析中，我们发现标准归一化 standard normalization减少了异常值的影响。

3.When training machine learning models, standard normalization 标准归一化 helps improve convergence speed.

在训练机器学习模型时，标准归一化 standard normalization有助于提高收敛速度。

4.In data preprocessing, we often apply standard normalization 标准归一化 to ensure that the features have a mean of zero and a standard deviation of one.

在数据预处理时，我们常常应用标准归一化 standard normalization来确保特征的均值为零，标准差为一。

5.Before performing PCA, it is essential to carry out standard normalization 标准归一化 on the dataset.

在进行主成分分析之前，必须对数据集进行标准归一化 standard normalization。

作文

In the field of data science and statistics, the term standard normalization refers to a process that transforms data into a standard format. This technique is crucial for ensuring that different datasets can be compared accurately and fairly. Without standard normalization (标准化), data from various sources may have different scales or units, which can lead to misleading results in analysis and modeling. For instance, consider a scenario where one dataset measures height in centimeters and another in inches. If we attempt to analyze these datasets together without standard normalization, we may draw incorrect conclusions due to the discrepancies in measurement units.The process of standard normalization typically involves adjusting the values in a dataset so that they fit within a certain range or distribution. A common method is to subtract the mean of the dataset from each data point and then divide by the standard deviation. This results in a dataset with a mean of 0 and a standard deviation of 1, which is particularly useful when applying machine learning algorithms. By applying standard normalization, we ensure that all features contribute equally to the analysis, preventing any single feature from disproportionately influencing the results.Moreover, standard normalization is not limited to numerical data. It can also be applied to categorical variables through techniques such as one-hot encoding, which transforms categorical data into a numerical format that can be easily analyzed. This versatility makes standard normalization an essential tool in the data preprocessing phase of any analytical project.Another significant advantage of standard normalization is its ability to improve the performance of machine learning models. Many algorithms, such as k-nearest neighbors and support vector machines, are sensitive to the scale of the input data. Without standard normalization, these models may perform poorly because they may give undue weight to features with larger ranges. By ensuring that all features are on a similar scale, we enhance the model's ability to learn from the data effectively.In conclusion, standard normalization (标准化) is a fundamental concept in data analysis that facilitates accurate comparisons and enhances the performance of machine learning models. As data continues to grow in complexity and volume, the importance of standard normalization will only increase. Understanding and implementing this technique is essential for anyone working in data science or related fields, as it lays the groundwork for robust and reliable analyses. Through proper application of standard normalization, we can unlock the true potential of our data and derive meaningful insights that drive decision-making and innovation.

在数据科学和统计学领域，短语标准化指的是将数据转换为标准格式的过程。这一技术对于确保不同数据集能够准确和公正地进行比较至关重要。如果没有标准化，来自不同来源的数据可能具有不同的尺度或单位，这可能导致分析和建模中的误导性结果。例如，考虑一个场景，其中一个数据集以厘米为单位测量身高，而另一个数据集以英寸为单位。如果我们试图在没有标准化的情况下一起分析这些数据集，我们可能会因为测量单位的差异而得出不正确的结论。标准化的过程通常涉及调整数据集中值，以使它们适合某个特定范围或分布。一种常见的方法是从每个数据点中减去数据集的均值，然后除以标准差。这样就会得到一个均值为0、标准差为1的数据集，这在应用机器学习算法时尤其有用。通过应用标准化，我们确保所有特征对分析的贡献是相等的，防止任何单一特征对结果产生不成比例的影响。此外，标准化不仅限于数值数据。它还可以通过诸如独热编码等技术应用于分类变量，后者将分类数据转换为可以轻松分析的数值格式。这种多功能性使得标准化成为任何分析项目数据预处理阶段的基本工具。标准化的另一个显著优势是它能够提高机器学习模型的性能。许多算法，如k近邻和支持向量机，对输入数据的规模非常敏感。如果没有标准化，这些模型可能表现不佳，因为它们可能会对具有较大范围的特征给予过多的权重。通过确保所有特征处于类似的尺度上，我们增强了模型有效学习数据的能力。总之，标准化(标准化)是数据分析中的一个基本概念，它促进了准确的比较并增强了机器学习模型的性能。随着数据的复杂性和数量不断增长，标准化的重要性只会增加。理解和实施这一技术对于任何从事数据科学或相关领域的人来说都是必不可少的，因为它为稳健可靠的分析奠定了基础。通过正确应用标准化，我们可以释放数据的真正潜力，并获得推动决策和创新的有意义的洞察。