高校英文法と機械学習例文



## 📌はじめに

 生成AIを使って「わからない部分」を聞いてみよう


## 📌【他動詞っぽい「自動詞」】一覧と例文（前置詞付き）

| 自動詞         | 意味     | 用法             | 機械学習例文（ML文脈）                                                               |
| ----------- | ------ | -------------- | -------------------------------------------------------------------------- |
| look        | 見る     | look at        | The analyst looked at the loss curve during training.                      |
| listen      | 聞く     | listen to      | Engineers listened to feedback from the model users.                       |
| glance      | ちらっと見る | glance at      | He glanced at the ROC curve before moving to the next experiment.          |
| stare       | じっと見る  | stare at       | She stared at the anomaly in the scatter plot.                             |
| wait        | 待つ     | wait for       | The script is waiting for the GPU to initialize.                           |
| participate | 参加する   | participate in | Many students participated in the Kaggle competition.                      |
| refrain     | 控える    | refrain from   | Please refrain from changing the model architecture during the test phase. |
| compete     | 競争する   | compete with   | This algorithm can now compete with state-of-the-art methods.              |
| major       | 専攻する   | major in       | He majored in machine learning and natural language processing.            |
| deal        | 扱う     | deal with      | We must deal with imbalanced data in binary classification.                |
| cope        | 対処する   | cope with      | The optimizer copes with vanishing gradients in deep networks.             |
| think       | 考える    | think of/about | We are thinking about applying reinforcement learning to robotics.         |
| hope        | 望む     | hope for       | We hope for a lower error rate in the validation set.                      |
| long        | 切望する   | long for       | Researchers long for a breakthrough in explainable AI.                     |
| dream       | 夢見る    | dream of/about | Many engineers dream of building general AI.                               |
| complain    | 不平を言う  | complain about | Some users complained about the model's bias.                              |
| insist      | 主張する   | insist on      | The author insists on using a probabilistic framework.                     |
| decide      | 決定する   | decide on      | The team decided on using the Adam optimizer instead of SGD.               |

---

## 📌【自動詞っぽい「他動詞」】一覧と例文（目的語を直にとる）

| 他動詞      | 意味       | 用法例                          | 機械学習例文（ML文脈）                                                            |
| -------- | -------- | ---------------------------- | ----------------------------------------------------------------------- |
| visit    | を訪れる     | visit a dataset              | We visited the UCI repository to download datasets.                     |
| discuss  | について議論する | discuss the algorithm        | The paper discusses the drawbacks of the naive Bayes classifier.        |
| enter    | に入る      | enter the data pipeline      | The input enters the preprocessing pipeline.                            |
| marry    | と結婚する    | marry the two methods        | We marry CNNs with RNNs in a hybrid architecture.                       |
| leave    | を去る      | leave the training phase     | The model left the training phase and entered evaluation.               |
| reach    | に到着する    | reach convergence            | The training process reached convergence after 20 epochs.               |
| approach | に近づく     | approach optimal performance | The new method approaches optimal performance on large datasets.        |
| mention  | に言及する    | mention the issue            | The authors mention overfitting in Section 3.                           |
| inhabit  | に住む      | inhabit the domain space     | Outliers inhabit a very different region of the feature space.          |
| answer   | に答える     | answer the question          | The model answers the user’s question using a large language model.     |
| face     | に直面する    | face a challenge             | The model faces the challenge of real-time prediction.                  |
| consider | を考慮する    | consider all features        | Consider all features before applying dimensionality reduction.         |
| follow   | に従う      | follow the protocol          | Please follow the preprocessing protocol strictly.                      |
| resemble | に似ている    | resemble the training data   | The test data must resemble the training data to ensure generalization. |
| match    | に合う      | match the input shape        | This tensor doesn’t match the input shape expected by the model.        |
| suit     | に似合う     | suit the task                | This model suits image classification better than regression.           |
| greet    | に挨拶する    | greet the participants       | The host greeted the participants at the AI workshop.                   |

---

## 📌【意味が大きく変わる動詞】例文（自動詞と他動詞の意味の差異を明示）

| 動詞        | 自動詞の意味     | 他動詞の意味 | 機械学習例文（ML文脈）                                                   |
| --------- | ---------- | ------ | -------------------------------------------------------------- |
| stand     | 立つ         | を我慢する  | The AI system cannot stand ambiguous input.                    |
| run       | 走る         | を運営する  | He runs a startup that builds large-scale ML systems.          |
| do        | 十分である（自動詞） | を行う    | This data augmentation does the job well.                      |
| succeed   | 成功する       | の後を継ぐ  | GPT-4 succeeded GPT-3 with better language capabilities.       |
| yield     | 屈する        | をもたらす  | This approach yields better F1 scores than previous ones.      |
| attend    | 注意を払う      | に出席する  | The professor attended the conference on Bayesian inference.   |
| associate | 付き合う、関係する  | を関連づける | People often associate overfitting with high model complexity. |

---



## 📌 自動詞と他動詞で**語形が異なる動詞ペア** 一覧（英語学習＋ML文脈）

| 日本語        | 自動詞                               | 他動詞                                  | 自動詞例文（ML文脈）                                                            | 他動詞例文（ML文脈）                                                      |
| ---------- | --------------------------------- | ------------------------------------ | ---------------------------------------------------------------------- | ---------------------------------------------------------------- |
| 横たわる／横たえる  | lie – lay – lain – lying          | lay – laid – laid – laying           | After debugging, the engineer lay down on the office couch.            | The developer laid the sensor module on the circuit board.       |
| 上がる／上げる    | rise – rose – risen – rising      | raise – raised – raised – raising    | GPU usage rose sharply during model training.                          | They raised the learning rate to speed up convergence.           |
| 座る／座らせる    | sit – sat – sat – sitting         | seat – seated – seated – seating     | She sat in front of the terminal to review the training logs.          | The system automatically seated the user in a virtual room.      |
| 起きる／起こす    | rise（同上）                          | raise（同上）                            | An unexpected error rose during batch processing.                      | The script raised an exception in line 52.                       |
| 倒れる／倒す     | fall – fell – fallen – falling    | drop – dropped – dropped – dropping  | The accuracy suddenly fell after epoch 20.                             | He dropped the model from the pipeline due to poor performance.  |
| 集まる／集める    | gather（群れる）                       | collect（集める）                         | Developers gathered around the whiteboard to discuss the architecture. | We collected over 10,000 annotated images for training.          |
| 始まる／始める    | begin – began – begun – beginning | start – started – started – starting | The conference begins at 9 a.m.                                        | We started training the model with new hyperparameters.          |
| 変わる／変える    | change（自・他で共有）                    | modify（他動詞のみ）                        | The system automatically changed due to the new update.                | The engineer modified the activation function from ReLU to GELU. |
| 到着する／到着させる | arrive（自動詞）                       | bring（連れてくる）                         | The data arrived at the server at 3 a.m.                               | The script brought all necessary files into the workspace.       |

---




## 📘 前置詞 **to + Ving** をとる表現一覧

| No | 表現                                 | 意味                           | 機械学習・ビジネス文脈の例文                                               |
| -- | ---------------------------------- | ---------------------------- | ------------------------------------------------------------ |
| 1  | **look forward to Ving**           | Vするのを楽しみに待つ                  | We look forward to deploying the model next month.           |
| 2a | **be used \[accustomed] to Ving**  | Vするのに慣れている                   | The team is used to tuning hyperparameters manually.         |
| 2b | **get used \[accustomed] to Ving** | Vするのに慣れる                     | You'll soon get used to working with large datasets.         |
| 3  | **object to Ving**                 | Vすることに反対する                   | Some users object to sharing personal data with the model.   |
| 4  | **What do you say to Ving?**       | Vしませんか                       | What do you say to trying ensemble learning?                 |
| 5  | **when it comes to Ving**          | Vすることになると                    | When it comes to labeling data, quality is crucial.          |
| 6  | **with a view to Ving**            | Vする目的で（= for the purpose of） | We collected more samples with a view to improving accuracy. |
| 7  | **devote oneself to Ving**         | Vするのに没頭する                    | She devoted herself to developing ethical AI systems.        |

---




## 📘 名詞節と副詞節の両方を導く接続詞一覧

| No | 接続詞                   | 副詞節の意味（条件や時間）   | 名詞節の意味（that節相当）   |
| -- | --------------------- | --------------- | ----------------- |
| 1  | **when SV**           | SがVするとき（時）      | いつSがVするか（疑問詞的）    |
| 2  | **if SV**             | もしSがVするならば（条件）  | SがVするかどうか（yes/no） |
| 3  | **whether SV or not** | SがVしようとしまいと（譲歩） | SがVするかどうか（選択）     |

---

## ✏️ 例文（機械学習・テクノロジー文脈）

### 1. **when SV**

* **副詞節**:
  *When the model finishes training*, the evaluation will start.
  → モデルの訓練が終了したとき、評価が始まる。

* **名詞節**:
  I don't know *when the server will respond*.
  → サーバーがいつ応答するか分からない。

---

### 2. **if SV**

* **副詞節**:
  *If you tune the parameters carefully*, performance will improve.
  → パラメータを丁寧に調整すれば、性能が向上する。

* **名詞節**:
  We need to check *if the dataset has missing values*.
  → データセットに欠損値があるかどうか確認する必要がある。

---

### 3. **whether SV or not**

* **副詞節**:
  We'll proceed *whether the results are promising or not*.
  → 結果が有望であろうとなかろうと、我々は前に進む。

* **名詞節**:
  It’s unclear *whether the model generalizes well or not*.
  → そのモデルがうまく汎化するかどうかは不明である。

---





## 📘 結果を表す不定詞 まとめ + 英語例文（技術文脈）

| No | 表現                                           | 日本語の意味            | 機械学習・ビジネス例文（英語）                                                              |
| -- | -------------------------------------------- | ----------------- | ---------------------------------------------------------------------------- |
| 1  | **grow up to be \~**                         | 成長して〜になる          | She grew up to be a leading researcher in AI ethics.                         |
| 2  | **live to be + 年齢**                          | 〜歳まで生きる           | Alan Turing lived to be only 41, but his legacy continues.                   |
| 3  | **awake to find \~**<br>= wake up to find \~ | 目が覚めると〜と気づく       | The team awoke to find that the entire dataset had been corrupted overnight. |
| 4  | **..., only to V**                           | ...だが、結局Vしただけだった  | He spent months training the model, only to overfit in the final test.       |
| 5  | **..., never to V**                          | ...し、そして二度とVしなかった | The system failed during deployment, never to recover.                       |

---



## 📘 「〜と関係がある／ない」という表現一覧

| No | 表現                              | 日本語訳        | 機械学習・ビジネス英語文例                                                             |
| -- | ------------------------------- | ----------- | ------------------------------------------------------------------------- |
| 1  | **have nothing to do with \~**  | ～とまったく関係がない | This error has nothing to do with the model architecture.                 |
| 2  | **have a lot to do with \~**    | ～と大いに関係がある  | Model performance has a lot to do with data quality.                      |
| 3  | **have a little to do with \~** | ～と少し関係がある   | The outliers may have a little to do with noise in sensor data.           |
| 4  | **have little to do with \~**   | ～とほとんど関係がない | The dropout rate has little to do with the optimizer choice in this case. |

---

### ✅ 使用の注意点：

* **a little** と **little** の違い：

  * **a little** = 肯定的ニュアンス（少しはある）
  * **little** = 否定的ニュアンス（ほとんどない）

---



## 📘 不可算名詞ベスト10（数えられない名詞）

| No | 不可算名詞                 | 意味   | ML・ビジネス英語例文                                                          |
| -- | --------------------- | ---- | -------------------------------------------------------------------- |
| 1  | **information**       | 情報   | The model was trained on user information.                           |
| 2  | **furniture**         | 家具   | The new office came with all necessary furniture.                    |
| 3  | **money**             | お金   | We don’t have enough money to deploy more GPUs.                      |
| 4  | **advice**            | 助言   | The mentor gave us useful advice on algorithm design.                |
| 5  | **news**              | ニュース | There is exciting news about the latest AI breakthrough.             |
| 6  | **homework**          | 宿題   | Your machine learning homework is due tomorrow.                      |
| 7  | **baggage / luggage** | 手荷物  | The researchers arrived with heavy luggage filled with devices.      |
| 8  | **poetry**            | 詩歌   | The AI attempted to generate poetry in the style of Emily Dickinson. |
| 9  | **scenery**           | 風景   | The drone captured beautiful scenery during the data collection.     |
| 10 | **clothing**          | 衣類   | The company developed smart clothing with biometric sensors.         |

---

## 🔄 紛らわしい可算 / 不可算 名詞（意味は似ている）

| 可算名詞                | 不可算名詞                 | 意味             | ML・技術英語例文                                                             |
| ------------------- | --------------------- | -------------- | --------------------------------------------------------------------- |
| **coin**            | **money**             | 硬貨 / お金        | This coin has no value, but the money in the wallet does.             |
| **bill / notes**    | **money**             | 紙幣 / お金        | The notes were counterfeit; real money is needed to purchase servers. |
| **chair / table**   | **furniture**         | 椅子 / 家具        | Each desk is separate, but furniture as a whole was preinstalled.     |
| **suitcase / bag**  | **baggage / luggage** | カバン / 手荷物      | He brought three bags, but all his luggage was checked in.            |
| **clothes / shoes** | **clothing**          | 衣服 / 衣類        | Clothes are scattered, but the clothing style is consistent.          |
| **assignments**     | **homework**          | 宿題（個別）/ 宿題（全体） | The homework includes three assignments due next week.                |

---



## 📘 1. **まぎらわしい動詞**（borrow / rent / take / bring など）

| 動詞         | ML文脈での例文                                                        |
| ---------- | --------------------------------------------------------------- |
| **borrow** | We borrowed a pre-trained model from the open-source community. |
| **rent**   | Our team rented extra cloud GPUs to accelerate training.        |
| **use**    | You can use the pretrained weights for fine-tuning.             |
| **take**   | I’ll take the dataset to the external storage server.           |
| **bring**  | Could you bring the model checkpoint to the meeting room?       |

---

## 📘 2. **first を含む表現**

| 表現                     | ML文脈での例文                                                     |
| ---------------------- | ------------------------------------------------------------ |
| **first of all**       | First of all, we need to clean the training data.            |
| **for the first time** | I deployed a transformer model for the first time yesterday. |
| **at first**           | At first, the accuracy fluctuated a lot during early epochs. |

---

## 📘 3. **「決して〜ではない」を表す表現**

| 表現                   | ML文脈での例文                                               |
| -------------------- | ------------------------------------------------------ |
| **far from \~**      | The model is far from being production-ready.          |
| **anything but \~**  | The debugging process was anything but simple.         |
| **not \~ at all**    | The model isn’t interpretable at all.                  |
| **by no means \~**   | This architecture is by no means optimal.              |
| **not in the least** | I’m not in the least concerned about overfitting here. |
| **not \~ whatever**  | There’s no performance gain whatever after tuning.     |
| **in no way \~**     | In no way should we deploy this model without testing. |

---

## 📘 4. **同格の that を従える名詞**

| 名詞          | ML文脈での例文                                                                |
| ----------- | ----------------------------------------------------------------------- |
| **fact**    | The fact that the model overfits suggests data leakage.                 |
| **news**    | The news that OpenAI released a new model shocked the team.             |
| **chance**  | There's a chance that the loss will diverge.                            |
| **belief**  | The belief that bigger models always perform better is misleading.      |
| **idea**    | The idea that AI can reason like humans is still under debate.          |
| **opinion** | In my opinion that model selection is more important than architecture. |
| **hope**    | We have hope that the new data augmentation will improve robustness.    |
| **rumor**   | There’s a rumor that the benchmark was leaked online.                   |

---

## 📘 5. **動名詞（Ving）を目的語にとる動詞（句）**

| 動詞（句）                | ML文脈での例文                                                   |
| -------------------- | ---------------------------------------------------------- |
| **mind Ving**        | Do you mind retraining the model overnight?                |
| **enjoy Ving**       | I enjoy tuning hyperparameters manually.                   |
| **give up Ving**     | We gave up using that optimizer after several failed runs. |
| **avoid Ving**       | We avoided using large batch sizes to reduce overfitting.  |
| **finish Ving**      | I just finished labeling all the images.                   |
| **escape being Vpp** | The model escaped being penalized by the regularizer.      |
| **postpone Ving**    | We postponed running the final evaluation due to a bug.    |
| **stop Ving**        | He stopped training after the validation loss plateaued.   |
| **deny Ving**        | The developer denied modifying the source code.            |
| **admit Ving**       | She admitted forgetting to normalize the input.            |
| **practice Ving**    | We practiced tuning under different noise conditions.      |
| **advise Ving**      | I advise trying a simpler model first.                     |
| **miss Ving**        | We missed capturing the edge cases in the dataset.         |
| **suggest Ving**     | I suggest logging all experiments using MLflow.            |
| **consider Ving**    | We’re considering switching to PyTorch Lightning.          |


---

## 📘 1. tell / talk / speak / say の使い分け（命令・報告・会話）

| 動詞        | 用法説明                      | ML文脈での例文                                               |
| --------- | ------------------------- | ------------------------------------------------------ |
| **tell**  | tell 人 to V / that SV     | The lead told the intern to restart the training job.  |
| **talk**  | talk to 人 / talk about \~ | We talked to the DevOps team about deployment latency. |
| **speak** | speak to 人 / speak about  | He spoke about data augmentation in his presentation.  |
| **say**   | say (to 人) that SV        | She said that the loss function needs to be modified.  |

---

## 📘 2. 不定詞 vs 動名詞（意味の違いに注意）

| 動詞句               | 意味              | ML文脈例文                                               |
| ----------------- | --------------- | ---------------------------------------------------- |
| **remember to V** | これから〜することを覚えている | Remember to log the accuracy after each epoch.       |
| **remember Ving** | 過去に〜したのを覚えている   | I remember updating the config file.                 |
| **forget to V**   | 〜するのを忘れる        | Don’t forget to save the final model.                |
| **forget Ving**   | 〜したのを忘れる        | I forgot adjusting the learning rate yesterday.      |
| **try to V**      | 〜しようと努力する       | We tried to improve inference speed.                 |
| **try Ving**      | 試しに〜してみる        | Try using batch normalization before the activation. |
| **regret to V**   | 残念ながら〜する（未来）    | We regret to announce a delay in model release.      |
| **regret Ving**   | 〜したのを後悔する       | I regret not testing the validation set thoroughly.  |

---

## 📘 3. 数・量の比較表現

| 表現                      | 意味       | ML文脈例文                                                |
| ----------------------- | -------- | ----------------------------------------------------- |
| **no more than \~**     | たった〜しかない | The model has no more than 2 million parameters.      |
| **no less than \~**     | ～もある     | We used no less than 50,000 images for training.      |
| **no fewer than +可算名詞** | ～もある     | The dataset contains no fewer than 10,000 labels.     |
| **not more than \~**    | 多くても     | We expect not more than 3 hours for training.         |
| **not less than \~**    | 少なくとも    | You need not less than 8GB of VRAM to run this model. |

---

## 📘 4. 独立不定詞の表現（to不定詞が副詞句のように働く）

| 表現                        | 意味／使い方     | ML文脈例文                                                     |
| ------------------------- | ---------- | ---------------------------------------------------------- |
| **to begin with**         | まず第一に      | To begin with, let's prepare the environment.              |
| **to tell the truth**     | 実を言うと      | To tell the truth, I forgot to back up the model.          |
| **so to speak**           | いわば        | This function is, so to speak, the brain of the pipeline.  |
| **strange to say**        | 奇妙な話だが     | Strange to say, the training loss decreased too quickly.   |
| **to make matters worse** | さらに悪いことに   | To make matters worse, the validation data was corrupted.  |
| **needless to say**       | 言うまでもなく    | Needless to say, data quality affects performance.         |
| **to say nothing of \~**  | ～は言うまでもなく  | The code is slow, to say nothing of the memory usage.      |
| **to be frank with you**  | 率直に言って     | To be frank with you, the model architecture is outdated.  |
| **to be sure**            | 確かに        | To be sure, the results look promising, but we need tests. |
| **not to say \~**         | ～とは言わないまでも | It's risky, not to say completely wrong.                   |
| **to say the least**      | 控えめに言っても   | The results are impressive, to say the least.              |
| **to do + 人 + justice**   | 公正に評価すれば   | This summary doesn’t do the algorithm justice.             |




---

## 📘1. 他動詞＋O＋to不定詞パターン

| 表現                   | 意味              | ML文脈例文                                                |
| -------------------- | --------------- | ----------------------------------------------------- |
| **allow O to V**     | OがVするのを許す       | The system allows users to download the model.        |
| **advise O to V**    | OがVするように助言する    | We advised the team to validate the dataset first.    |
| **ask O to V**       | OがVするように頼む      | They asked the intern to rerun the training.          |
| **cause O to V**     | OにVさせる          | The bug caused the server to crash.                   |
| **enable O to V**    | OがVするのを可能にする    | This optimization enables the model to train faster.  |
| **encourage O to V** | OがVするように励ます     | The results encouraged us to continue tuning.         |
| **force O to V**     | OにVさせる          | We had to force the model to stop due to overfitting. |
| **tell O to V**      | OにVするように言う、命令する | I told the script to resume from checkpoint.          |

---

## 📘2. 助動詞＋have＋過去分詞（過去の推量・後悔）

| 表現                              | 意味        | ML文脈例文                                                  |
| ------------------------------- | --------- | ------------------------------------------------------- |
| **must have Vpp**               | 〜したに違いない  | The optimizer must have converged by now.               |
| **should \[ought to] have Vpp** | ～すべきだった   | We should have saved the logs.                          |
| **may have Vpp**                | ～したかもしれない | The update may have introduced a bug.                   |
| **might have Vpp**              | ～したかもしれない | The model might have overfit the dev set.               |
| **cannot have Vpp**             | ～したはずがない  | The validation loss cannot have decreased that quickly. |

### 過去の行為を悔やむ表現

| 表現                              | 意味         | ML文脈例文                                       |
| ------------------------------- | ---------- | -------------------------------------------- |
| **need not have Vpp**           | ～する必要はなかった | We need not have retrained the entire model. |
| **should \[ought to] have Vpp** | ～すべきだった    | You should have double-checked the labels.   |
| **had better have Vpp**         | ～したほうがよかった | You had better have backed up the config.    |

---

## 📘3. to不定詞をとる自動詞・他動詞

### 自動詞＋to不定詞

| 表現                      | 意味       | ML文脈例文                                        |
| ----------------------- | -------- | --------------------------------------------- |
| **happen to V**         | たまたま〜する  | I happened to find a better loss function.    |
| **prove (to be) \~**    | ～だとわかる   | The model proved to be robust to noise.       |
| **turn out (to be) \~** | ～だと判明する  | The result turned out to be a data leakage.   |
| **come/get to V**       | Vするようになる | The system got to outperform human baselines. |

### 他動詞＋to不定詞

| 表現              | 意味          | ML文脈例文                                 |
| --------------- | ----------- | -------------------------------------- |
| **manage to V** | なんとかVする     | We managed to reduce training time.    |
| **hope to V**   | ～することを希望する  | I hope to publish the paper soon.      |
| **refuse to V** | ～するのを拒む     | The model refused to converge.         |
| **offer to V**  | ～することを申し出る  | He offered to label the data manually. |
| **decide to V** | ～することを決める   | They decided to use PyTorch instead.   |
| **expect to V** | ～すると思う／予想する | We expect to reach 95% accuracy.       |
| **intend to V** | ～するつもりである   | I intend to test the new architecture. |
| **mean to V**   | ～するつもりである   | I didn’t mean to stop the training.    |

---

## 📘4. 分詞構文を用いた慣用表現（～ing）

| 表現                     | 意味        | ML文脈例文                                                     |
| ---------------------- | --------- | ---------------------------------------------------------- |
| **strictly speaking**  | 厳密に言えば    | Strictly speaking, the model is a semi-supervised learner. |
| **frankly speaking**   | 率直に言えば    | Frankly speaking, the results are underwhelming.           |
| **generally speaking** | 一般的に言えば   | Generally speaking, CNNs perform well on image tasks.      |
| **roughly speaking**   | おおざっぱに言えば | Roughly speaking, the accuracy is around 85%.              |
| **speaking of \~**     | ～といえば     | Speaking of overfitting, have you tried dropout?           |
| **judging from \~**    | ～から判断すると  | Judging from the logs, the model has converged.            |
| **including \~**       | ～を含めて     | Including augmented data improved performance.             |
| **considering \~**     | ～を考慮すると   | Considering the limited data, the result is good.          |
| **compared with \~**   | ～と比べると    | Compared with baseline models, ours is faster.             |
| **given \~**           | ～を考慮すると   | Given the time constraints, this is sufficient.            |

### 独立分詞構文

| 表現                                 | 意味         | ML文脈例文                                                     |
| ---------------------------------- | ---------- | ---------------------------------------------------------- |
| **weather permitting**             | 天候がよければ    | Weather permitting, we’ll test the drone model outside.    |
| **all things considered**          | すべてを考慮すれば  | All things considered, the results are acceptable.         |
| **such being the case**            | そういう状況であれば | Such being the case, we will retrain the model.            |
| **(all) other things being equal** | 他の条件が同じなら  | Other things being equal, larger models generalize better. |

---

## 📘5. 比較級・最上級・代名詞 those

### 最上級・比較級の文例（富士山・時間をベースにした例）

| 表現                                   | ML文脈例文                                                  |
| ------------------------------------ | ------------------------------------------------------- |
| **X is the most accurate model.**    | GPT-4 is the most accurate model we've tested.          |
| **X is better than any other Y.**    | This optimizer is faster than any other tested so far.  |
| **No other model is better than X.** | No other model is better than GPT-4 in text generation. |
| **No model is as fast as X.**        | No model is as fast as this distilled version.          |

### 名詞の反復を避ける代名詞

| 名詞の種類        | 代名詞         | ML文脈例文                                                             |
| ------------ | ----------- | ------------------------------------------------------------------ |
| a + 名詞       | **one**     | I prefer this one over the older model.                            |
| 可算名詞の複数形     | **ones**    | I tested both models and chose the newer ones.                     |
| the + 名詞     | **the one** | The model we deployed is the one trained last week.                |
| the + 名詞の複数形 | **those**   | The results of Experiment A are more reliable than **those** of B. |

---



## 📘1. 「ときどき」を表す副詞表現（頻度）

| 表現                       | 意味    | ML文脈例文                                            |
| ------------------------ | ----- | ------------------------------------------------- |
| **from time to time**    | ときどき  | We update the dataset from time to time.          |
| **(every) now and then** | ときどき  | I retrain the model now and then.                 |
| **now and again**        | ときどき  | The server crashes now and again.                 |
| **at times**             | ときどき  | At times, the validation accuracy drops suddenly. |
| **occasionally**         | ときおり  | The optimizer occasionally diverges.              |
| **sometimes**            | ときどき  | Sometimes the model fails to converge.            |
| **once in a while**      | ごくたまに | We test the backup model once in a while.         |

---

## 📘2. 比較表現：to を使った「よりも〜」

| 表現                      | 意味       | ML文脈例文                                                              |
| ----------------------- | -------- | ------------------------------------------------------------------- |
| **be superior to \~**   | ～より優れている | Transformer is superior to RNN in language tasks.                   |
| **be inferior to \~**   | ～より劣っている | This lightweight model is inferior to the full version in accuracy. |
| **be senior to \~**     | ～より年上    | The senior engineer is more experienced in model tuning.            |
| **be junior to \~**     | ～より年下    | The intern is junior to the project lead.                           |
| **prefer A to B**       | BよりAを好む  | We prefer PyTorch to TensorFlow for its flexibility.                |
| **be preferable to \~** | ～より好ましい  | A smaller model is preferable to a complex one in production.       |

---

## 📘3. 過去を示す語句（明確に過去時制を要求）

| 表現            | 意味     | ML文脈例文                                   |
| ------------- | ------ | ---------------------------------------- |
| **yesterday** | 昨日     | I fixed the data leak yesterday.         |
| **last \~**   | この前の〜  | The bug was introduced last week.        |
| **then**      | そのとき   | The model crashed then without warning.  |
| **\~ ago**    | ～前     | We released the dataset two months ago.  |
| **when**      | ～だったとき | I noticed the issue when I ran the test. |
| **just now**  | たった今   | The loss dropped just now.               |

---

## 📘4. 形式主語構文に書き換え可能な形容詞（It is 〜 to V）

| 形容詞                | 意味   | ML文脈例文（形式主語）                                           |
| ------------------ | ---- | ------------------------------------------------------ |
| **easy**           | 簡単な  | It is easy to deploy this model.                       |
| **impossible**     | 不可能な | It is impossible to train on this device.              |
| **convenient**     | 便利な  | It is convenient to use a pre-trained model.           |
| **difficult/hard** | 難しい  | It is hard to optimize this function.                  |
| **pleasant**       | 心地よい | It is pleasant to work with a well-documented API.     |
| **dangerous**      | 危険な  | It is dangerous to ignore data imbalance.              |
| **comfortable**    | 快適な  | It is comfortable to test in a controlled environment. |


---

## 📘1. 相互複数（互いに～する）表現まとめ

| 表現                          | 意味         | ML文脈例文                                                                |
| --------------------------- | ---------- | --------------------------------------------------------------------- |
| **change trains**           | 乗り換える（電車）  | We had to change servers like changing trains during model migration. |
| **make friends (with \~)**  | ～と友達になる    | He made friends with researchers at the ML conference.                |
| **exchange seats**          | 席を取り替える    | The engineers exchanged seats to pair program efficiently.            |
| **be on ... terms with \~** | ～とは…な間柄である | We are on good terms with the open-source community.                  |
| **shake hands**             | 握手する       | They shook hands after agreeing on the API standard.                  |
| **take turns (at) Ving**    | 交代で～する     | We took turns reviewing the training logs overnight.                  |

---

## 📘2. 仮定法現在（that節内で動詞の原形）

仮定法現在とは、**主節の動詞にかかわらず、that節で原形動詞または should + V が使われる**構文。特に**命令・提案・要求・主張**を表す動詞の後でよく使われます。

### 主節の動詞分類と機械学習文脈例文

| 種類     | 主節の動詞例                      | that節（ML例文）                                              |
| ------ | --------------------------- | -------------------------------------------------------- |
| **命令** | order                       | I ordered that the script be terminated immediately.     |
| **決定** | determine, decide           | We decided that the pipeline start from scratch.         |
| **要求** | demand, require, request    | They demanded that the model be retrained with new data. |
| **主張** | insist                      | She insisted that the results be verified independently. |
| **提案** | suggest, propose, recommend | I suggest that the logs be saved every epoch.            |

> ※英語（イギリス式）では "should + V"：I suggest that the model **should be** evaluated again.
> ※アメリカ英語では "V原形" が一般的：I suggest that the model **be** evaluated again.

---