[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HAQSQ6FWJWHCPVBKTYZBPR":3,"skill-same:01M6HAQSQ6FWJWHCPVBKTYZBPR":56},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6HAQSQ6FWJWHCPVBKTYZBPR","statistical-learning",null,"Statistical learning methods","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fstatistical-learning\u002FSKILL.md",[],[19,20,21,22,23,24,25],"learning","statistical learning","machine-learning","opencode","methods","intelligence","machine","2026-11-10 10:12:30",false,"---\nname: statistical-learning\ndescription: Statistical learning methods\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: machine-learning-engineers\n  category: artificial-intelligence\n---\n\n## What I do\n\n- Apply statistical methods to learning problems\n- Build regression and classification models\n- Perform hypothesis testing and inference\n- Estimate model parameters\n- Evaluate model uncertainty\n- Design experiments and A\u002FB tests\n\n## When to use me\n\nUse me when:\n- Building predictive models with uncertainty\n- Interpreting model decisions\n- Designing experiments\n- Making data-driven decisions\n- Understanding model confidence\n\n## Key Concepts\n\n### Regression Methods\n```python\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.model_selection import cross_val_score\n\n# Simple Linear Regression\nmodel = LinearRegression()\nmodel.fit(X, y)\ny_pred = model.predict(X)\n\n# Regularized Regression\nridge = Ridge(alpha=1.0)  # L2 penalty\nlasso = Lasso(alpha=1.0)  # L1 penalty\n\n# Polynomial Features\npoly = PolynomialFeatures(degree=2, include_bias=False)\nX_poly = poly.fit_transform(X)\n\n# Cross-validation\nscores = cross_val_score(model, X, y, cv=5, scoring='r2')\n```\n\n### Classification\n```python\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n\n# Logistic Regression\nlog_reg = LogisticRegression()\nlog_reg.fit(X, y)\n\n# Probabilistic outputs\nprobs = log_reg.predict_proba(X)\npred = log_reg.predict(X)\n\n# Naive Bayes (generative)\nnb = GaussianNB()\nnb.fit(X, y)\n\n# LDA (discriminative)\nlda = LinearDiscriminantAnalysis()\nlda.fit(X, y)\n```\n\n### Model Evaluation\n```python\nfrom sklearn.metrics import (accuracy_score, precision_score, \n    recall_score, f1_score, roc_auc_score, confusion_matrix)\n\n# Classification metrics\naccuracy = accuracy_score(y_true, y_pred)\nprecision = precision_score(y_true, y_pred, average='weighted')\nrecall = recall_score(y_true, y_pred, average='weighted')\nf1 = f1_score(y_true, y_pred, average='weighted')\nauc = roc_auc_score(y_true, probas)\n\n# Regression metrics\nfrom sklearn.metrics import mean_squared_error, r2_score\nmse = mean_squared_error(y_true, y_pred)\nr2 = r2_score(y_true, y_pred)\n```\n\n### Statistical Inference\n- Confidence intervals\n- Hypothesis testing (t-test, ANOVA)\n- P-values and significance\n- Bootstrap methods\n- Bayesian inference\n","## What I do\n\n- Apply statistical methods to learning problems\n- Build regression and classification models\n- Perform hypothesis testing and inference\n- Estimate model parameters\n- Evaluate model uncertainty\n- Design experiments and A\u002FB tests\n\n## When to use me\n\nUse me when:\n- Building predictive models with uncertainty\n- Interpreting model decisions\n- Designing experiments\n- Making data-driven decisions\n- Understanding model confidence\n\n## Key Concepts\n\n### Regression Methods\n```python\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.model_selection import cross_val_score\n\n# Simple Linear Regression\nmodel = LinearRegression()\nmodel.fit(X, y)\ny_pred = model.predict(X)\n\n# Regularized Regression\nridge = Ridge(alpha=1.0)  # L2 penalty\nlasso = Lasso(alpha=1.0)  # L1 penalty\n\n# Polynomial Features\npoly = PolynomialFeatures(degree=2, include_bias=False)\nX_poly = poly.fit_transform(X)\n\n# Cross-validation\nscores = cross_val_score(model, X, y, cv=5, scoring='r2')\n```\n\n### Classification\n```python\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n\n# Logistic Regression\nlog_reg = LogisticRegression()\nlog_reg.fit(X, y)\n\n# Probabilistic outputs\nprobs = log_reg.predict_proba(X)\npred = log_reg.predict(X)\n\n# Naive Bayes (generative)\nnb = GaussianNB()\nnb.fit(X, y)\n\n# LDA (discriminative)\nlda = LinearDiscriminantAnalysis()\nlda.fit(X, y)\n```\n\n### Model Evaluation\n```python\nfrom sklearn.metrics import (accuracy_score, precision_score, \n    recall_score, f1_score, roc_auc_score, confusion_matrix)\n\n# Classification metrics\naccuracy = accuracy_score(y_true, y_pred)\nprecision = precision_score(y_true, y_pred, average='weighted')\nrecall = recall_score(y_true, y_pred, average='weighted')\nf1 = f1_score(y_true, y_pred, average='weighted')\nauc = roc_auc_score(y_true, probas)\n\n# Regression metrics\nfrom sklearn.metrics import mean_squared_error, r2_score\nmse = mean_squared_error(y_true, y_pred)\nr2 = r2_score(y_true, y_pred)\n```\n\n### Statistical Inference\n- Confidence intervals\n- Hypothesis testing (t-test, ANOVA)\n- P-values and significance\n- Bootstrap methods\n- Bayesian inference\n",{"name":5,"license":15,"metadata":31,"description":7,"compatibility":22},{"audience":32,"category":33},"machine-learning-engineers","artificial-intelligence","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Fstatistical-learning","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Theoretical and Futuristic AI Nerd 🤓 ","2026-04-21 14:37:05",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add NeuralBlitz\u002FMito --skill 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