Pyecharts.Dataset.COUNTRIES

[
    "Afghanistan",
    "Albania",
    "Algeria",
    "Andorra",
    "Angola",
    "Anguilla",
    "Antigua and Barbuda",
    "Argentina",
    "Armenia",
    "Aruba",
    "Australia",
    "Austria",
    "Azerbaijan",
    "Bahamas",
    "Bahrain",
    "Bangladesh",
    "Barbados",
    "Belarus",
    "Belgium",
    "Belize",
    "Benin",
    "Bermuda",
    "Bhutan",
    "Bolivia",
    "Botswana",
    "Brazil",
    "British Indian Ocean Territory",
    "British Virgin Islands",
    "Brunei",
    "Bulgaria",
    "Burkina Faso",
    "Burundi",
    "Cambodia",
    "Cameroon",
    "Canada",
    "Cape Verde",
    "Cayman Islands",
    "Central African Republic",
    "Chad",
    "Chile",
    "China",
    "Colombia",
    "Comoros",
    "Costa Rica",
    "Croatia",
    "Cuba",
    "Curaçao",
    "Cyprus",
    "Czechia",
    "Democratic Republic of the Congo",
    "Denmark",
    "Djibouti",
    "Dominica",
    "Dominican Republic",
    "East Timor",
    "Ecuador",
    "Egypt",
    "El Salvador",
    "Equatorial Guinea",
    "Eritrea",
    "Estonia",
    "Eswatini",
    "Ethiopia",
    "Falkland Islands",
    "Faroe Islands",
    "Fiji",
    "Finland",
    "France",
    "French Southern and Antarctic Lands",
    "Gabon",
    "Gambia",
    "Georgia",
    "Germany",
    "Ghana",
    "Greece",
    "Greenland",
    "Grenada",
    "Guam",
    "Guatemala",
    "Guernsey",
    "Guinea",
    "Guinea-Bissau",
    "Guyana",
    "Haiti",
    "Heard Island and McDonald Islands",
    "Honduras",
    "Hungary",
    "Iceland",
    "India",
    "Indonesia",
    "Iran",
    "Iraq",
    "Ireland",
    "Isle of Man",
    "Israel",
    "Italy",
    "Ivory Coast",
    "Jamaica",
    "Japan",
    "Jersey",
    "Jordan",
    "Kazakhstan",
    "Kenya",
    "Kiribati",
    "Kosovo",
    "Kuwait",
    "Kyrgyzstan",
    "Latvia",
    "Lebanon",
    "Lesotho",
    "Liberia",
    "Libya",
    "Liechtenstein",
    "Lithuania",
    "Luxembourg",
    "Madagascar",
    "Malawi",
    "Malaysia",
    "Maldives",
    "Mali",
    "Malta",
    "Marshall Islands",
    "Mauritania",
    "Mauritius",
    "Mexico",
    "Micronesia",
    "Moldova",
    "Mongolia",
    "Montenegro",
    "Montserrat",
    "Morocco",
    "Mozambique",
    "Myanmar",
    "Namibia",
    "Nepal",
    "Netherlands",
    "New Caledonia",
    "New Zealand",
    "Nicaragua",
    "Niger",
    "Nigeria",
    "North Korea",
    "North Macedonia",
    "Northern Cyprus",
    "Northern Mariana Islands",
    "Norway",
    "Oman",
    "Pakistan",
    "Palau",
    "Palestine",
    "Panama",
    "Papua New Guinea",
    "Paraguay",
    "Peru",
    "Philippines",
    "Poland",
    "Portugal",
    "Puerto Rico",
    "Qatar",
    "Republic of the Congo",
    "Romania",
    "Russia",
    "Rwanda",
    "Saint Barthélemy",
    "Saint Helena",
    "Saint Kitts and Nevis",
    "Saint Lucia",
    "Saint Martin (French part)",
    "Saint Pierre and Miquelon",
    "Saint Vincent and the Grenadines",
    "Samoa",
    "San Marino",
    "Saudi Arabia",
    "Senegal",
    "Serbia",
    "Seychelles",
    "Siachen Glacier",
    "Sierra Leone",
    "Singapore",
    "Sint Maarten (Dutch part)",
    "Slovakia",
    "Slovenia",
    "Solomon Islands",
    "Somalia",
    "Somaliland",
    "South Africa",
    "South Georgia and the South Sandwich Islands",
    "South Korea",
    "South Sudan",
    "Spain",
    "Sri Lanka",
    "Sudan",
    "Suriname",
    "Sweden",
    "Switzerland",
    "Syria",
    "São Tomé and Príncipe",
    "Tajikistan",
    "Tanzania",
    "Thailand",
    "Togo",
    "Tonga",
    "Trinidad and Tobago",
    "Tunisia",
    "Turkey",
    "Turkmenistan",
    "Turks and Caicos Islands",
    "Uganda",
    "Ukraine",
    "United Arab Emirates",
    "United States",
    "United States Virgin Islands",
    "United Kingdom",
    "Uruguay",
    "Uzbekistan",
    "Vanuatu",
    "Venezuela",
    "Vietnam",
    "Western Sahara",
    "Yemen",
    "Zambia",
    "Zimbabwe",
    "Åland Islands"
]

内容概要:本文档详细介绍了基于SABO-SVR减法平均算法(SABO)优化支持向量机回归的数据多输入单输出回归预测项目。项目旨在通过引入SABO算法优化SVR模型,提高其预测精度和计算效率,解决传统SVR在处理复杂非线性关系和高维数据时的局限性。文档涵盖了项目背景、目标与意义、挑战及解决方案、特点与创新、应用领域、效果预测图及程序设计、模型架构、代码示例、注意事项、未来改进方向等内容。项目通过优化计算效率、增强非线性建模能力、自动化优化过程等创新点,为多个领域提供了高效的回归预测解决方案。 适合人群:具备一定机器学习基础,尤其是对支持向量机回归(SVR)和优化算法感兴趣的工程师、研究人员及数据科学家。 使用场景及目标:①优化SVR模型,提高其在复杂数据集上的预测精度和计算效率;②解决多输入单输出回归问题,如金融、能源、制造业、医疗健康、环境监测等领域的大规模数据分析;③通过引入SABO算法,避免局部最优解,实现全局优化;④提供自动化优化过程,减少人工调参工作量。 其他说明:项目不仅实现了SABO-SVR模型的构建与优化,还提供了详细的代码示例和GUI设计,帮助用户更好地理解和应用该技术。此外,文档还探讨了模型的可扩展性、实时预测优化、跨平台支持等未来改进方向,确保项目在实际应用中的高效性和前瞻性。
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